Modifier and Type | Class and Description |
---|---|
class |
AbstractAssociator
Abstract scheme for learning associations.
|
class |
Apriori
Class implementing an Apriori-type algorithm.
|
class |
AprioriItemSet
Class for storing a set of items.
|
class |
AssociatorEvaluation
Class for evaluating Associaters.
|
class |
CaRuleGeneration
Class implementing the rule generation procedure of the predictive apriori algorithm for class association rules.
|
class |
CheckAssociator
Class for examining the capabilities and finding problems with
associators.
|
class |
FilteredAssociator
Class for running an arbitrary associator on data that has been passed through an arbitrary filter.
|
class |
FPGrowth
Class implementing the FP-growth algorithm for finding large item sets without candidate generation.
|
class |
GeneralizedSequentialPatterns
Class implementing a GSP algorithm for discovering sequential patterns in a sequential data set.
The attribute identifying the distinct data sequences contained in the set can be determined by the respective option. |
class |
ItemSet
Class for storing a set of items.
|
class |
LabeledItemSet
Class for storing a set of items together with a class label.
|
class |
PredictiveApriori
Class implementing the predictive apriori algorithm to mine association rules.
It searches with an increasing support threshold for the best 'n' rules concerning a support-based corrected confidence value. For more information see: Tobias Scheffer: Finding Association Rules That Trade Support Optimally against Confidence. |
class |
PriorEstimation
Class implementing the prior estimattion of the predictive apriori algorithm
for mining association rules.
|
class |
RuleGeneration
Class implementing the rule generation procedure of the predictive apriori algorithm.
|
class |
RuleItem
Class for storing an (class) association rule.
|
class |
SingleAssociatorEnhancer
Abstract utility class for handling settings common to meta
associators that use a single base associator.
|
class |
Tertius
Finds rules according to confirmation measure (Tertius-type algorithm).
For more information see: P. |
Modifier and Type | Class and Description |
---|---|
class |
Element
Class representing an Element, i.e., a set of events/items.
|
class |
Sequence
Class representing a sequence of elements/itemsets.
|
Modifier and Type | Class and Description |
---|---|
class |
AttributeValueLiteral |
class |
Body
Class representing the body of a rule.
|
class |
Head
Class representing the head of a rule.
|
class |
IndividualInstance |
class |
IndividualInstances |
class |
IndividualLiteral |
class |
Literal |
class |
LiteralSet
Class representing a set of literals, being either the body or the head
of a rule.
|
class |
Predicate |
class |
Rule
Class representing a rule with a body and a head.
|
class |
SimpleLinkedList |
class |
SimpleLinkedList.LinkedListInverseIterator |
class |
SimpleLinkedList.LinkedListIterator |
Modifier and Type | Class and Description |
---|---|
class |
ASEvaluation
Abstract attribute selection evaluation class
|
class |
ASSearch
Abstract attribute selection search class.
|
class |
AttributeSelection
Attribute selection class.
|
class |
AttributeSetEvaluator
Abstract attribute set evaluator.
|
class |
BestFirst
BestFirst:
Searches the space of attribute subsets by greedy hillclimbing augmented with a backtracking facility. |
class |
BestFirst.Link2
Class for a node in a linked list.
|
class |
BestFirst.LinkedList2
Class for handling a linked list.
|
class |
CfsSubsetEval
CfsSubsetEval :
Evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them. Subsets of features that are highly correlated with the class while having low intercorrelation are preferred. For more information see: M. |
class |
CheckAttributeSelection
Class for examining the capabilities and finding problems with
attribute selection schemes.
|
class |
ChiSquaredAttributeEval
ChiSquaredAttributeEval :
Evaluates the worth of an attribute by computing the value of the chi-squared statistic with respect to the class. Valid options are: |
class |
ClassifierSubsetEval
Classifier subset evaluator:
Evaluates attribute subsets on training data or a seperate hold out testing set. |
class |
ConsistencySubsetEval
ConsistencySubsetEval :
Evaluates the worth of a subset of attributes by the level of consistency in the class values when the training instances are projected onto the subset of attributes. |
class |
ConsistencySubsetEval.hashKey
Class providing keys to the hash table.
|
class |
CostSensitiveASEvaluation
Abstract base class for cost-sensitive subset and attribute evaluators.
|
class |
CostSensitiveAttributeEval
A meta subset evaluator that makes its base subset evaluator cost-sensitive.
|
class |
CostSensitiveSubsetEval
A meta subset evaluator that makes its base subset evaluator cost-sensitive.
|
class |
ExhaustiveSearch
ExhaustiveSearch :
Performs an exhaustive search through the space of attribute subsets starting from the empty set of attrubutes. |
class |
FilteredAttributeEval
Class for running an arbitrary attribute evaluator on data that has been passed through an
arbitrary filter (note: filters that alter the order or number of attributes are not allowed).
|
class |
FilteredSubsetEval
Class for running an arbitrary subset evaluator on data that has been passed through an arbitrary
filter (note: filters that alter the order or number of attributes are not allowed).
|
class |
GainRatioAttributeEval
GainRatioAttributeEval :
Evaluates the worth of an attribute by measuring the gain ratio with respect to the class. GainR(Class, Attribute) = (H(Class) - H(Class | Attribute)) / H(Attribute). Valid options are: |
class |
GeneticSearch
GeneticSearch:
Performs a search using the simple genetic algorithm described in Goldberg (1989). For more information see: David E. |
class |
GreedyStepwise
GreedyStepwise :
Performs a greedy forward or backward search through the space of attribute subsets. |
class |
HoldOutSubsetEvaluator
Abstract attribute subset evaluator capable of evaluating subsets with
respect to a data set that is distinct from that used to initialize/
train the subset evaluator.
|
class |
InfoGainAttributeEval
InfoGainAttributeEval :
Evaluates the worth of an attribute by measuring the information gain with respect to the class. InfoGain(Class,Attribute) = H(Class) - H(Class | Attribute). Valid options are: |
class |
LatentSemanticAnalysis
Performs latent semantic analysis and transformation of the data.
|
class |
LFSMethods |
class |
LFSMethods.Link2
Class for a node in a linked list.
|
class |
LFSMethods.LinkedList2
Class for handling a linked list.
|
class |
LinearForwardSelection
LinearForwardSelection:
Extension of BestFirst. |
class |
OneRAttributeEval
OneRAttributeEval :
Evaluates the worth of an attribute by using the OneR classifier. Valid options are: |
class |
PrincipalComponents
Performs a principal components analysis and transformation of the data.
|
class |
RaceSearch
Races the cross validation error of competing attribute subsets.
|
class |
RandomSearch
RandomSearch :
Performs a Random search in the space of attribute subsets. |
class |
Ranker
Ranker :
Ranks attributes by their individual evaluations. |
class |
RankSearch
RankSearch :
Uses an attribute/subset evaluator to rank all attributes. |
class |
ReliefFAttributeEval
ReliefFAttributeEval :
Evaluates the worth of an attribute by repeatedly sampling an instance and considering the value of the given attribute for the nearest instance of the same and different class. |
class |
ScatterSearchV1
Class for performing the Sequential Scatter Search.
|
class |
SubsetSizeForwardSelection
SubsetSizeForwardSelection:
Extension of LinearForwardSelection. |
class |
SVMAttributeEval
SVMAttributeEval :
Evaluates the worth of an attribute by using an SVM classifier. |
class |
SymmetricalUncertAttributeEval
SymmetricalUncertAttributeEval :
Evaluates the worth of an attribute by measuring the symmetrical uncertainty with respect to the class. |
class |
UnsupervisedAttributeEvaluator
Abstract unsupervised attribute evaluator.
|
class |
UnsupervisedSubsetEvaluator
Abstract unsupervised attribute subset evaluator.
|
class |
WrapperSubsetEval
WrapperSubsetEval:
Evaluates attribute sets by using a learning scheme. |
Modifier and Type | Class and Description |
---|---|
class |
BVDecompose
Class for performing a Bias-Variance decomposition on any classifier using the method specified in:
Ron Kohavi, David H. |
class |
BVDecomposeSegCVSub
This class performs Bias-Variance decomposion on any classifier using the sub-sampled cross-validation procedure as specified in (1).
The Kohavi and Wolpert definition of bias and variance is specified in (2). The Webb definition of bias and variance is specified in (3). Geoffrey I. |
class |
CheckClassifier
Class for examining the capabilities and finding problems with
classifiers.
|
class |
Classifier
Abstract classifier.
|
class |
CostMatrix
Class for storing and manipulating a misclassification cost matrix.
|
class |
Evaluation
Class for evaluating machine learning models.
|
class |
IteratedSingleClassifierEnhancer
Abstract utility class for handling settings common to
meta classifiers that build an ensemble from a single base learner.
|
class |
MultipleClassifiersCombiner
Abstract utility class for handling settings common to
meta classifiers that build an ensemble from multiple classifiers.
|
class |
RandomizableClassifier
Abstract utility class for handling settings common to randomizable
classifiers.
|
class |
RandomizableIteratedSingleClassifierEnhancer
Abstract utility class for handling settings common to randomizable
meta classifiers that build an ensemble from a single base learner.
|
class |
RandomizableMultipleClassifiersCombiner
Abstract utility class for handling settings common to randomizable
meta classifiers that build an ensemble from multiple classifiers based
on a given random number seed.
|
class |
RandomizableSingleClassifierEnhancer
Abstract utility class for handling settings common to randomizable
meta classifiers that build an ensemble from a single base learner.
|
class |
SingleClassifierEnhancer
Abstract utility class for handling settings common to meta
classifiers that use a single base learner.
|
Modifier and Type | Class and Description |
---|---|
class |
AODE
AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes.
|
class |
AODEsr
AODEsr augments AODE with Subsumption Resolution.AODEsr detects specializations between two attribute values at classification time and deletes the generalization attribute value.
For more information, see: Fei Zheng, Geoffrey I. |
class |
BayesianLogisticRegression
Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors.
For more information, see Alexander Genkin, David D. |
class |
ComplementNaiveBayes
Class for building and using a Complement class Naive Bayes classifier.
For more information see, Jason D. |
class |
DMNBtext
Class for building and using a Discriminative Multinomial Naive Bayes classifier.
|
class |
HNB
Contructs Hidden Naive Bayes classification model with high classification accuracy and AUC.
For more information refer to: H. |
class |
NaiveBayes
Class for a Naive Bayes classifier using estimator classes.
|
class |
NaiveBayesMultinomial
Class for building and using a multinomial Naive Bayes classifier.
|
class |
NaiveBayesMultinomialUpdateable
Class for building and using a multinomial Naive Bayes classifier.
|
class |
NaiveBayesSimple
Class for building and using a simple Naive Bayes classifier.Numeric attributes are modelled by a normal distribution.
For more information, see Richard Duda, Peter Hart (1973). |
class |
NaiveBayesUpdateable
Class for a Naive Bayes classifier using estimator classes.
|
class |
WAODE
WAODE contructs the model called Weightily Averaged One-Dependence Estimators.
For more information, see L. |
Modifier and Type | Class and Description |
---|---|
class |
GaussianPriorImpl
Implementation of the Gaussian Prior update function based on
CLG Algorithm with a certain Trust Region Update.
|
class |
LaplacePriorImpl
Implementation of the Gaussian Prior update function based on modified
CLG Algorithm (CLG-Lasso) with a certain Trust Region Update based
on Laplace Priors.
|
class |
Prior
This is an interface to plug various priors into
the Bayesian Logistic Regression Model.
|
Modifier and Type | Class and Description |
---|---|
class |
ADNode
The ADNode class implements the ADTree datastructure which increases
the speed with which sub-contingency tables can be constructed from
a data set in an Instances object.
|
class |
BayesNetGenerator
Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier. |
class |
BIFReader
Builds a description of a Bayes Net classifier stored in XML BIF 0.3 format.
For more details on XML BIF see: Fabio Cozman, Marek Druzdzel, Daniel Garcia (1998). |
class |
EditableBayesNet
Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier. |
class |
MarginCalculator |
class |
MarginCalculator.JunctionTreeNode |
class |
MarginCalculator.JunctionTreeSeparator |
class |
ParentSet
Helper class for Bayes Network classifiers.
|
class |
VaryNode
Part of ADTree implementation.
|
Modifier and Type | Class and Description |
---|---|
class |
BayesNetEstimator
BayesNetEstimator is the base class for estimating the conditional probability tables of a Bayes network once the structure has been learned.
|
class |
BMAEstimator
BMAEstimator estimates conditional probability tables of a Bayes network using Bayes Model Averaging (BMA).
|
class |
DiscreteEstimatorBayes
Symbolic probability estimator based on symbol counts and a prior.
|
class |
DiscreteEstimatorFullBayes
Symbolic probability estimator based on symbol counts and a prior.
|
class |
MultiNomialBMAEstimator
Multinomial BMA Estimator.
|
class |
SimpleEstimator
SimpleEstimator is used for estimating the conditional probability tables of a Bayes network once the structure has been learned.
|
Modifier and Type | Class and Description |
---|---|
class |
SearchAlgorithm
This is the base class for all search algorithms for learning Bayes networks.
|
Modifier and Type | Class and Description |
---|---|
class |
CISearchAlgorithm
The CISearchAlgorithm class supports Bayes net structure search algorithms that are based on conditional independence test (as opposed to for example score based of cross validation based search algorithms).
|
class |
ICSSearchAlgorithm
This Bayes Network learning algorithm uses conditional independence tests to find a skeleton, finds V-nodes and applies a set of rules to find the directions of the remaining arrows.
|
Modifier and Type | Class and Description |
---|---|
class |
FromFile
The FromFile reads the structure of a Bayes net from a file in BIFF format.
|
Modifier and Type | Class and Description |
---|---|
class |
GlobalScoreSearchAlgorithm
This Bayes Network learning algorithm uses cross validation to estimate classification accuracy.
|
class |
HillClimber
This Bayes Network learning algorithm uses a hill climbing algorithm adding, deleting and reversing arcs.
|
class |
K2
This Bayes Network learning algorithm uses a hill climbing algorithm restricted by an order on the variables.
For more information see: G.F. |
class |
RepeatedHillClimber
This Bayes Network learning algorithm repeatedly uses hill climbing starting with a randomly generated network structure and return the best structure of the various runs.
|
class |
SimulatedAnnealing
This Bayes Network learning algorithm uses the general purpose search method of simulated annealing to find a well scoring network structure.
For more information see: R.R. |
class |
TabuSearch
This Bayes Network learning algorithm uses tabu search for finding a well scoring Bayes network structure.
|
class |
TAN
This Bayes Network learning algorithm determines the maximum weight spanning tree and returns a Naive Bayes network augmented with a tree.
For more information see: N. |
Modifier and Type | Class and Description |
---|---|
class |
LAGDHillClimber
This Bayes Network learning algorithm uses a Look Ahead Hill Climbing algorithm called LAGD Hill Climbing.
|
class |
LocalScoreSearchAlgorithm
The ScoreBasedSearchAlgorithm class supports Bayes net structure search algorithms that are based on maximizing scores (as opposed to for example conditional independence based search algorithms).
|
Modifier and Type | Class and Description |
---|---|
class |
ConfusionMatrix
Cells of this matrix correspond to counts of the number (or weight)
of predictions for each actual value / predicted value combination.
|
class |
CostCurve
Generates points illustrating probablity cost tradeoffs that can be
obtained by varying the threshold value between classes.
|
class |
EvaluationUtils
Contains utility functions for generating lists of predictions in
various manners.
|
class |
MarginCurve
Generates points illustrating the prediction margin.
|
class |
NominalPrediction
Encapsulates an evaluatable nominal prediction: the predicted probability
distribution plus the actual class value.
|
class |
NumericPrediction
Encapsulates an evaluatable numeric prediction: the predicted class value
plus the actual class value.
|
class |
ThresholdCurve
Generates points illustrating prediction tradeoffs that can be obtained
by varying the threshold value between classes.
|
class |
TwoClassStats
Encapsulates performance functions for two-class problems.
|
Modifier and Type | Class and Description |
---|---|
class |
GaussianProcesses
Implements Gaussian Processes for regression without hyperparameter-tuning.
|
class |
IsotonicRegression
Learns an isotonic regression model.
|
class |
LeastMedSq
Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions.
|
class |
LibLINEAR
A wrapper class for the liblinear tools (the liblinear classes, typically the jar file, need to be in the classpath to use this classifier).
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (2008). |
class |
LibSVM
A wrapper class for the libsvm tools (the libsvm
classes, typically the jar file, need to be in the classpath to use this
classifier).
LibSVM runs faster than SMO since it uses LibSVM to build the SVM classifier. LibSVM allows users to experiment with One-class SVM, Regressing SVM, and nu-SVM supported by LibSVM tool. |
class |
Logistic
Class for building and using a multinomial logistic regression model with a ridge estimator.
There are some modifications, however, compared to the paper of leCessie and van Houwelingen(1992): If there are k classes for n instances with m attributes, the parameter matrix B to be calculated will be an m*(k-1) matrix. The probability for class j with the exception of the last class is Pj(Xi) = exp(XiBj)/((sum[j=1..(k-1)]exp(Xi*Bj))+1) The last class has probability 1-(sum[j=1..(k-1)]Pj(Xi)) = 1/((sum[j=1..(k-1)]exp(Xi*Bj))+1) The (negative) multinomial log-likelihood is thus: L = -sum[i=1..n]{ sum[j=1..(k-1)](Yij * ln(Pj(Xi))) +(1 - (sum[j=1..(k-1)]Yij)) * ln(1 - sum[j=1..(k-1)]Pj(Xi)) } + ridge * (B^2) In order to find the matrix B for which L is minimised, a Quasi-Newton Method is used to search for the optimized values of the m*(k-1) variables. |
class |
MultilayerPerceptron
A Classifier that uses backpropagation to classify instances.
This network can be built by hand, created by an algorithm or both. |
class |
PaceRegression
Class for building pace regression linear models and using them for prediction.
|
class |
PLSClassifier
A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions.
|
class |
RBFNetwork
Class that implements a normalized Gaussian radial basisbasis function network.
It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. |
class |
SimpleLinearRegression
Learns a simple linear regression model.
|
class |
SimpleLogistic
Classifier for building linear logistic regression models.
|
class |
SMO
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.
This implementation globally replaces all missing values and transforms nominal attributes into binary ones. |
class |
SMOreg
SMOreg implements the support vector machine for regression.
|
class |
SPegasos
Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al.
|
class |
VotedPerceptron
Implementation of the voted perceptron algorithm by Freund and Schapire.
|
class |
Winnow
Implements Winnow and Balanced Winnow algorithms by Littlestone.
For more information, see N. |
Modifier and Type | Class and Description |
---|---|
class |
LinearUnit
This can be used by the
neuralnode to perform all it's computations (as a Linear unit).
|
class |
NeuralConnection
Abstract unit in a NeuralNetwork.
|
class |
NeuralNode
This class is used to represent a node in the neuralnet.
|
class |
SigmoidUnit
This can be used by the
neuralnode to perform all it's computations (as a sigmoid unit).
|
Modifier and Type | Class and Description |
---|---|
class |
ChisqMixture
Class for manipulating chi-square mixture distributions.
|
class |
DiscreteFunction
Class for handling discrete functions.
|
class |
MixtureDistribution
Abtract class for manipulating mixture distributions.
|
class |
NormalMixture
Class for manipulating normal mixture distributions.
|
class |
PaceMatrix
Class for matrix manipulation used for pace regression.
|
Modifier and Type | Class and Description |
---|---|
class |
CachedKernel
Base class for RBFKernel and PolyKernel that implements a simple LRU.
|
class |
CheckKernel
Class for examining the capabilities and finding problems with
kernels.
|
class |
Kernel
Abstract kernel.
|
class |
KernelEvaluation
Class for evaluating Kernels.
|
class |
NormalizedPolyKernel
The normalized polynomial kernel.
K(x,y) = <x,y>/sqrt(<x,x><y,y>) where <x,y> = PolyKernel(x,y) Valid options are: |
class |
PolyKernel
The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p
Valid options are:
|
class |
PrecomputedKernelMatrixKernel
This kernel is based on a static kernel matrix that is read from a file.
|
class |
Puk
The Pearson VII function-based universal kernel.
For more information see: B. |
class |
RBFKernel
The RBF kernel.
|
class |
RegOptimizer
Base class implementation for learning algorithm of SMOreg
Valid options are:
|
class |
RegSMO
Implementation of SMO for support vector regression as described in :
A.J. |
class |
RegSMOImproved
Learn SVM for regression using SMO with Shevade, Keerthi, et al.
|
class |
SMOset
Stores a set of integer of a given size.
|
class |
StringKernel
Implementation of the subsequence kernel (SSK) as described in [1] and of the subsequence kernel with lambda pruning (SSK-LP) as described in [2].
For more information, see Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J. |
Modifier and Type | Class and Description |
---|---|
class |
IB1
Nearest-neighbour classifier.
|
class |
IBk
K-nearest neighbours classifier.
|
class |
KStar
K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function.
|
class |
LBR
Lazy Bayesian Rules Classifier.
|
class |
LBR.Indexes
Class for handling instances and the associated attributes.
|
class |
LWL
Locally weighted learning.
|
Modifier and Type | Class and Description |
---|---|
class |
KStarCache
A class representing the caching system used to keep track of each attribute
value and its corresponding scale factor or stop parameter.
|
class |
KStarCache.CacheTable
A custom hashtable class to support the caching system.
|
class |
KStarCache.TableEntry
Hashtable collision list.
|
class |
KStarNominalAttribute
A custom class which provides the environment for computing the
transformation probability of a specified test instance nominal
attribute to a specified train instance nominal attribute.
|
class |
KStarNumericAttribute
A custom class which provides the environment for computing the
transformation probability of a specified test instance numeric
attribute to a specified train instance numeric attribute.
|
class |
KStarWrapper |
Modifier and Type | Class and Description |
---|---|
class |
AdaBoostM1
Class for boosting a nominal class classifier using the Adaboost M1 method.
|
class |
AdditiveRegression
Meta classifier that enhances the performance of a regression base classifier.
|
class |
AttributeSelectedClassifier
Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
|
class |
Bagging
Class for bagging a classifier to reduce variance.
|
class |
ClassificationViaClustering
A simple meta-classifier that uses a clusterer for classification.
|
class |
ClassificationViaRegression
Class for doing classification using regression methods.
|
class |
CostSensitiveClassifier
A metaclassifier that makes its base classifier cost-sensitive.
|
class |
CVParameterSelection
Class for performing parameter selection by cross-validation for any classifier.
For more information, see: R. |
class |
Dagging
This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier.
|
class |
Decorate
DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.
|
class |
END
A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies.
For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. |
class |
FilteredClassifier
Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
|
class |
Grading
Implements Grading.
|
class |
GridSearch
Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting.
The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy). |
class |
LogitBoost
Class for performing additive logistic regression.
|
class |
MetaCost
This metaclassifier makes its base classifier cost-sensitive using the method specified in
Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive. |
class |
MultiBoostAB
Class for boosting a classifier using the MultiBoosting method.
MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. |
class |
MultiClassClassifier
A metaclassifier for handling multi-class datasets with 2-class classifiers.
|
class |
MultiScheme
Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
|
class |
OrdinalClassClassifier
Meta classifier that allows standard classification algorithms to be applied to ordinal class problems.
For more information see: Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification. |
class |
RacedIncrementalLogitBoost
Classifier for incremental learning of large datasets by way of racing logit-boosted committees.
For more information see: Eibe Frank, Geoffrey Holmes, Richard Kirkby, Mark Hall: Racing committees for large datasets. |
class |
RandomCommittee
Class for building an ensemble of randomizable base classifiers.
|
class |
RandomSubSpace
This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
|
class |
RegressionByDiscretization
A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.
|
class |
RotationForest
Class for construction a Rotation Forest.
|
class |
Stacking
Combines several classifiers using the stacking method.
|
class |
StackingC
Implements StackingC (more efficient version of stacking).
For more information, see A.K. |
class |
ThresholdSelector
A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier.
|
class |
Vote
Class for combining classifiers.
|
Modifier and Type | Class and Description |
---|---|
class |
ClassBalancedND
A meta classifier for handling multi-class datasets with 2-class classifiers by building a random class-balanced tree structure.
For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. |
class |
DataNearBalancedND
A meta classifier for handling multi-class datasets with 2-class classifiers by building a random data-balanced tree structure.
For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. |
class |
ND
A meta classifier for handling multi-class datasets with 2-class classifiers by building a random tree structure.
For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. |
Modifier and Type | Class and Description |
---|---|
class |
CitationKNN
Modified version of the Citation kNN multi instance classifier.
For more information see: Jun Wang, Zucker, Jean-Daniel: Solving Multiple-Instance Problem: A Lazy Learning Approach. |
class |
MDD
Modified Diverse Density algorithm, with collective assumption.
More information about DD: Oded Maron (1998). |
class |
MIBoost
MI AdaBoost method, considers the geometric mean of posterior of instances inside a bag (arithmatic mean of log-posterior) and the expectation for a bag is taken inside the loss function.
For more information about Adaboost, see: Yoav Freund, Robert E. |
class |
MIDD
Re-implement the Diverse Density algorithm, changes the testing procedure.
Oded Maron (1998). |
class |
MIEMDD
EMDD model builds heavily upon Dietterich's Diverse Density (DD) algorithm.
It is a general framework for MI learning of converting the MI problem to a single-instance setting using EM. |
class |
MILR
Uses either standard or collective multi-instance assumption, but within linear regression.
|
class |
MINND
Multiple-Instance Nearest Neighbour with Distribution learner.
It uses gradient descent to find the weight for each dimension of each exeamplar from the starting point of 1.0. |
class |
MIOptimalBall
This classifier tries to find a suitable ball in the multiple-instance space, with a certain data point in the instance space as a ball center.
|
class |
MISMO
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.
This implementation globally replaces all missing values and transforms nominal attributes into binary ones. |
class |
MISVM
Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL).
|
class |
MIWrapper
A simple Wrapper method for applying standard propositional learners to multi-instance data.
For more information see: E. |
class |
SimpleMI
Reduces MI data into mono-instance data.
|
Modifier and Type | Class and Description |
---|---|
class |
MIPolyKernel
The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p
Valid options are:
|
class |
MIRBFKernel
The RBF kernel.
|
Modifier and Type | Class and Description |
---|---|
class |
HyperPipes
Class implementing a HyperPipe classifier.
|
class |
SerializedClassifier
A wrapper around a serialized classifier model.
|
class |
VFI
Classification by voting feature intervals.
|
Modifier and Type | Class and Description |
---|---|
class |
GeneralRegression
Class implementing import of PMML General Regression model.
|
class |
NeuralNetwork
Class implementing import of PMML Neural Network model.
|
class |
PMMLClassifier
Abstract base class for all PMML classifiers.
|
class |
Regression
Class implementing import of PMML Regression model.
|
Modifier and Type | Class and Description |
---|---|
class |
ConjunctiveRule
This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels.
A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. |
class |
DecisionTable
Class for building and using a simple decision table majority classifier.
For more information see: Ron Kohavi: The Power of Decision Tables. |
class |
DecisionTableHashKey
Class providing hash table keys for DecisionTable
|
class |
DTNB
Class for building and using a decision table/naive bayes hybrid classifier.
|
class |
JRip
This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W.
|
class |
JRip.Antd
The single antecedent in the rule, which is composed of an attribute and
the corresponding value.
|
class |
JRip.NominalAntd
The antecedent with nominal attribute
|
class |
JRip.NumericAntd
The antecedent with numeric attribute
|
class |
JRip.RipperRule
This class implements a single rule that predicts specified class.
|
class |
M5Rules
Generates a decision list for regression problems using separate-and-conquer.
|
class |
NNge
Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules).
|
class |
OneR
Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes.
|
class |
PART
Class for generating a PART decision list.
|
class |
Prism
Class for building and using a PRISM rule set for classification.
|
class |
Ridor
An implementation of a RIpple-DOwn Rule learner.
It generates a default rule first and then the exceptions for the default rule with the least (weighted) error rate. |
class |
RuleStats
This class implements the statistics functions used in the
propositional rule learner, from the simpler ones like count of
true/false positive/negatives, filter data based on the ruleset, etc.
|
class |
ZeroR
Class for building and using a 0-R classifier.
|
Modifier and Type | Class and Description |
---|---|
class |
C45PruneableDecList
Class for handling a partial tree structure pruned using C4.5's
pruning heuristic.
|
class |
ClassifierDecList
Class for handling a rule (partial tree) for a decision list.
|
class |
MakeDecList
Class for handling a decision list.
|
class |
PruneableDecList
Class for handling a partial tree structure that
can be pruned using a pruning set.
|
Modifier and Type | Class and Description |
---|---|
class |
ADTree
Class for generating an alternating decision tree.
|
class |
BFTree
Class for building a best-first decision tree classifier.
|
class |
DecisionStump
Class for building and using a decision stump.
|
class |
FT
Classifier for building 'Functional trees', which are classification trees that could have logistic regression functions at the inner nodes and/or leaves.
|
class |
Id3
Class for constructing an unpruned decision tree based on the ID3 algorithm.
|
class |
J48
Class for generating a pruned or unpruned C4.5 decision tree.
|
class |
J48graft
Class for generating a grafted (pruned or unpruned) C4.5 decision tree.
|
class |
LADTree
Class for generating a multi-class alternating decision tree using the LogitBoost strategy.
|
class |
LMT
Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves.
|
class |
M5P
M5Base.
|
class |
NBTree
Class for generating a decision tree with naive Bayes classifiers at the leaves.
For more information, see Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. |
class |
RandomForest
Class for constructing a forest of random trees.
For more information see: Leo Breiman (2001). |
class |
RandomTree
Class for constructing a tree that considers K
randomly chosen attributes at each node.
|
class |
REPTree
Fast decision tree learner.
|
class |
SimpleCart
Class implementing minimal cost-complexity pruning.
Note when dealing with missing values, use "fractional instances" method instead of surrogate split method. For more information, see: Leo Breiman, Jerome H. |
class |
UserClassifier
Interactively classify through visual means.
|
Modifier and Type | Class and Description |
---|---|
class |
PredictionNode
Class representing a prediction node in an alternating tree.
|
class |
ReferenceInstances
Simple class that extends the Instances class making it possible to create
subsets of instances that reference their source set.
|
class |
Splitter
Abstract class representing a splitter node in an alternating tree.
|
class |
TwoWayNominalSplit
Class representing a two-way split on a nominal attribute, of the form:
either 'is some_value' or 'is not some_value'.
|
class |
TwoWayNumericSplit
Class representing a two-way split on a numeric attribute, of the form:
either 'is < some_value' or 'is >= some_value'.
|
Modifier and Type | Class and Description |
---|---|
class |
FTInnerNode
Class for Functional Inner tree structure.
|
class |
FTLeavesNode
Class for Functional Leaves tree version.
|
class |
FTNode
Class for Functional tree structure.
|
class |
FTtree
Abstract class for Functional tree structure.
|
Modifier and Type | Class and Description |
---|---|
class |
BinC45ModelSelection
Class for selecting a C4.5-like binary (!) split for a given dataset.
|
class |
BinC45Split
Class implementing a binary C4.5-like split on an attribute.
|
class |
C45ModelSelection
Class for selecting a C4.5-type split for a given dataset.
|
class |
C45PruneableClassifierTree
Class for handling a tree structure that can
be pruned using C4.5 procedures.
|
class |
C45PruneableClassifierTreeG
Class for handling a tree structure that can
be pruned using C4.5 procedures and have nodes grafted on.
|
class |
C45Split
Class implementing a C4.5-type split on an attribute.
|
class |
ClassifierSplitModel
Abstract class for classification models that can be used
recursively to split the data.
|
class |
ClassifierTree
Class for handling a tree structure used for
classification.
|
class |
Distribution
Class for handling a distribution of class values.
|
class |
EntropyBasedSplitCrit
"Abstract" class for computing splitting criteria
based on the entropy of a class distribution.
|
class |
EntropySplitCrit
Class for computing the entropy for a given distribution.
|
class |
GainRatioSplitCrit
Class for computing the gain ratio for a given distribution.
|
class |
GraftSplit
Class implementing a split for nodes added to a tree during grafting.
|
class |
InfoGainSplitCrit
Class for computing the information gain for a given distribution.
|
class |
ModelSelection
Abstract class for model selection criteria.
|
class |
NBTreeClassifierTree
Class for handling a naive bayes tree structure used for
classification.
|
class |
NBTreeModelSelection
Class for selecting a NB tree split.
|
class |
NBTreeNoSplit
Class implementing a "no-split"-split (leaf node) for naive bayes
trees.
|
class |
NBTreeSplit
Class implementing a NBTree split on an attribute.
|
class |
NoSplit
Class implementing a "no-split"-split.
|
class |
PruneableClassifierTree
Class for handling a tree structure that can
be pruned using a pruning set.
|
class |
SplitCriterion
Abstract class for computing splitting criteria
with respect to distributions of class values.
|
Modifier and Type | Class and Description |
---|---|
class |
LMTNode
Class for logistic model tree structure.
|
class |
LogisticBase
Base/helper class for building logistic regression models with the LogitBoost algorithm.
|
class |
ResidualModelSelection
Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to implement the
splitting criterion based on residuals.
|
class |
ResidualSplit
Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to implement the
splitting criterion based on residuals of the LogitBoost algorithm.
|
Modifier and Type | Class and Description |
---|---|
class |
CorrelationSplitInfo
Finds split points using correlation.
|
class |
Impurity
Class for handling the impurity values when spliting the instances
|
class |
M5Base
M5Base.
|
class |
PreConstructedLinearModel
This class encapsulates a linear regression function.
|
class |
RuleNode
Constructs a node for use in an m5 tree or rule
|
class |
Values
Stores some statistics.
|
class |
YongSplitInfo
Stores split information.
|
Modifier and Type | Class and Description |
---|---|
class |
XMLClassifier
This class serializes and deserializes a Classifier instance to and
fro XML.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractClusterer
Abstract clusterer.
|
class |
AbstractDensityBasedClusterer
Abstract clustering model that produces (for each test instance)
an estimate of the membership in each cluster
(ie.
|
class |
CheckClusterer
Class for examining the capabilities and finding problems with
clusterers.
|
class |
CLOPE
Yiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional data.
|
class |
ClusterEvaluation
Class for evaluating clustering models.
Valid options are:
-t name of the training file
Specify the training file. |
class |
Cobweb
Class implementing the Cobweb and Classit clustering algorithms.
Note: the application of node operators (merging, splitting etc.) in terms of ordering and priority differs (and is somewhat ambiguous) between the original Cobweb and Classit papers. |
class |
DBSCAN
Basic implementation of DBSCAN clustering algorithm that should *not* be used as a reference for runtime benchmarks: more sophisticated implementations exist! Clustering of new instances is not supported.
|
class |
EM
Simple EM (expectation maximisation) class.
EM assigns a probability distribution to each instance which indicates the probability of it belonging to each of the clusters. |
class |
FarthestFirst
Cluster data using the FarthestFirst algorithm.
For more information see: Hochbaum, Shmoys (1985). |
class |
FilteredClusterer
Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter.
|
class |
HierarchicalClusterer
Hierarchical clustering class.
|
class |
MakeDensityBasedClusterer
Class for wrapping a Clusterer to make it return a distribution and density.
|
class |
OPTICS
Basic implementation of OPTICS clustering algorithm that should *not* be used as a reference for runtime benchmarks: more sophisticated implementations exist! Clustering of new instances is not supported.
|
class |
RandomizableClusterer
Abstract utility class for handling settings common to randomizable
clusterers.
|
class |
RandomizableDensityBasedClusterer
Abstract utility class for handling settings common to randomizable
clusterers.
|
class |
RandomizableSingleClustererEnhancer
Abstract utility class for handling settings common to randomizable
clusterers.
|
class |
sIB
Cluster data using the sequential information bottleneck algorithm.
Note: only hard clustering scheme is supported. |
class |
SimpleKMeans
Cluster data using the k means algorithm
Valid options are:
|
class |
SingleClustererEnhancer
Meta-clusterer for enhancing a base clusterer.
|
class |
XMeans
Cluster data using the X-means algorithm.
X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region. |
Modifier and Type | Class and Description |
---|---|
class |
SequentialDatabase
SequentialDatabase.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert Date: Aug 20, 2004 Time: 1:23:38 PM $ Revision 1.4 $ |
Modifier and Type | Class and Description |
---|---|
class |
EuclideanDataObject
EuclideanDataObject.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert Date: Aug 19, 2004 Time: 5:50:22 PM $ Revision 1.4 $ |
class |
ManhattanDataObject
ManhattanDataObject.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert Date: Aug 19, 2004 Time: 5:50:22 PM $ Revision 1.4 $ |
Modifier and Type | Class and Description |
---|---|
class |
GraphPanel
GraphPanel.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht Date: Sep 16, 2004 Time: 10:28:19 AM $ Revision 1.4 $ |
class |
OPTICS_Visualizer
Start the OPTICS Visualizer from command-line:
java weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.OPTICS_Visualizer [file.ser]
|
class |
ResultVectorTableModel
ResultVectorTableModel.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht Date: Sep 12, 2004 Time: 9:23:31 PM $ Revision 1.4 $ |
class |
SERFileFilter
SERFileFilter.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht Date: Sep 15, 2004 Time: 6:54:56 PM $ Revision 1.4 $ |
class |
SERObject
SERObject.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht Date: Sep 15, 2004 Time: 9:43:00 PM $ Revision 1.4 $ |
Modifier and Type | Class and Description |
---|---|
class |
EpsilonRange_ListElement
EpsilonRange_ListElement.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert Date: Sep 7, 2004 Time: 2:12:34 PM $ Revision 1.4 $ |
class |
PriorityQueue
PriorityQueue.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert Date: Aug 27, 2004 Time: 5:36:35 PM $ Revision 1.4 $ |
class |
PriorityQueueElement
PriorityQueueElement.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert Date: Aug 31, 2004 Time: 6:43:18 PM $ Revision 1.4 $ |
class |
UpdateQueue
UpdateQueue.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert Date: Aug 27, 2004 Time: 5:36:35 PM $ Revision 1.4 $ |
class |
UpdateQueueElement
UpdateQueueElement.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert Date: Aug 31, 2004 Time: 6:43:18 PM $ Revision 1.4 $ |
Modifier and Type | Class and Description |
---|---|
class |
AbstractStringDistanceFunction
Represents the abstract ancestor for string-based distance functions, like
EditDistance.
|
class |
AlgVector
Class for performing operations on an algebraic vector
of floating-point values.
|
class |
AllJavadoc
Applies all known Javadoc-derived classes to a source file.
|
class |
Attribute
Class for handling an attribute.
|
class |
AttributeExpression
A general purpose class for parsing mathematical expressions
involving attribute values.
|
class |
AttributeLocator
This class locates and records the indices of a certain type of attributes,
recursively in case of Relational attributes.
|
class |
AttributeStats
A Utility class that contains summary information on an
the values that appear in a dataset for a particular attribute.
|
class |
BinarySparseInstance
Class for storing a binary-data-only instance as a sparse vector.
|
class |
Capabilities
A class that describes the capabilites (e.g., handling certain types of
attributes, missing values, types of classes, etc.) of a specific
classifier.
|
class |
ChebyshevDistance
Implements the Chebyshev distance.
|
class |
Check
Abstract general class for testing in Weka.
|
class |
CheckGOE
Simple command line checking of classes that are editable in the GOE.
Usage:
CheckGOE -W classname -- test options
Valid options are: |
class |
CheckOptionHandler
Simple command line checking of classes that implement OptionHandler.
Usage:
CheckOptionHandler -W optionHandlerClassName -- test options
Valid options are: |
class |
CheckScheme
Abstract general class for testing schemes in Weka.
|
static class |
CheckScheme.PostProcessor
a class for postprocessing the test-data
|
class |
ClassDiscovery
This class is used for discovering classes that implement a certain
interface or a derived from a certain class.
|
static class |
ClassDiscovery.StringCompare
compares two strings.
|
class |
ClassloaderUtil
Utility class that can add jar files to the classpath dynamically.
|
class |
ContingencyTables
Class implementing some statistical routines for contingency tables.
|
class |
Debug
A helper class for debug output, logging, clocking, etc.
|
static class |
Debug.Clock
A little helper class for clocking and outputting times.
|
static class |
Debug.DBO
contains debug methods
|
static class |
Debug.Log
A helper class for logging stuff.
|
static class |
Debug.Random
This extended Random class enables one to print the generated random
numbers etc., before they are returned.
|
static class |
Debug.SimpleLog
A little, simple helper class for logging stuff.
|
static class |
Debug.Timestamp
A class that can be used for timestamps in files, The toString() method
simply returns the associated Date object in a timestamp format.
|
class |
EditDistance
Computes the Levenshtein edit distance between two strings.
|
class |
Environment
This class encapsulates a map of all environment and java system properties.
|
class |
EuclideanDistance
Implementing Euclidean distance (or similarity) function.
One object defines not one distance but the data model in which the distances between objects of that data model can be computed. Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low. For more information, see: Wikipedia. |
class |
FastVector
Implements a fast vector class without synchronized
methods.
|
class |
FastVector.FastVectorEnumeration
Class for enumerating the vector's elements.
|
class |
FindWithCapabilities
Locates all classes with certain capabilities.
|
class |
GlobalInfoJavadoc
Generates Javadoc comments from the class's globalInfo method.
|
class |
Instance
Class for handling an instance.
|
class |
InstanceComparator
A comparator for the Instance class.
|
class |
Instances
Class for handling an ordered set of weighted instances.
|
class |
Javadoc
Abstract superclass for classes that generate Javadoc comments and replace
the content between certain comment tags.
|
class |
Jython
A helper class for Jython.
|
class |
ListOptions
Lists the options of an OptionHandler
|
class |
ManhattanDistance
Implements the Manhattan distance (or Taxicab geometry).
|
class |
MathematicalExpression
Class for evaluating a string adhering the following grammar:
|
class |
Matrix
Deprecated.
Use
weka.core.matrix.Matrix instead - only for
backwards compatibility. |
class |
Memory
A little helper class for Memory management.
|
class |
NormalizableDistance
Represents the abstract ancestor for normalizable distance functions, like
Euclidean or Manhattan distance.
|
class |
Optimization
Implementation of Active-sets method with BFGS update to solve optimization
problem with only bounds constraints in multi-dimensions.
|
class |
Option
Class to store information about an option.
|
class |
OptionHandlerJavadoc
Generates Javadoc comments from the OptionHandler's options.
|
class |
PropertyPath
A helper class for accessing properties in nested objects, e.g., accessing
the "getRidge" method of a LinearRegression classifier part of
MultipleClassifierCombiner, e.g., Vote.
|
static class |
PropertyPath.Path
Contains a (property) path structure
|
static class |
PropertyPath.PathElement
Represents a single element of a property path
|
class |
ProtectedProperties
Simple class that extends the Properties class so that the properties are
unable to be modified.
|
class |
Queue
Class representing a FIFO queue.
|
class |
RandomVariates
Class implementing some simple random variates generator.
|
class |
Range
Class representing a range of cardinal numbers.
|
class |
RelationalLocator
This class locates and records the indices of relational attributes,
|
class |
SelectedTag
Represents a selected value from a finite set of values, where each
value is a Tag (i.e.
|
class |
SerializationHelper
A helper class for determining serialVersionUIDs and checking whether
classes contain one and/or need one.
|
class |
SerializedObject
Class for storing an object in serialized form in memory.
|
class |
SingleIndex
Class representing a single cardinal number.
|
class |
SparseInstance
Class for storing an instance as a sparse vector.
|
class |
SpecialFunctions
Class implementing some mathematical functions.
|
class |
Statistics
Class implementing some distributions, tests, etc.
|
class |
Stopwords
Class that can test whether a given string is a stop word.
|
class |
StringLocator
This class locates and records the indices of String attributes,
recursively in case of Relational attributes.
|
class |
SystemInfo
This class prints some information about the system setup, like Java
version, JVM settings etc.
|
class |
Tag
A
Tag simply associates a numeric ID with a String description. |
class |
TechnicalInformation
Used for paper references in the Javadoc and for BibTex generation.
|
class |
TechnicalInformationHandlerJavadoc
Generates Javadoc comments from the TechnicalInformationHandler's data.
|
class |
Tee
This class pipelines print/println's to several PrintStreams.
|
class |
TestInstances
Generates artificial datasets for testing.
|
class |
Trie
A class representing a Trie data structure for strings.
|
static class |
Trie.TrieIterator
Represents an iterator over a trie
|
static class |
Trie.TrieNode
Represents a node in the trie.
|
class |
Utils
Class implementing some simple utility methods.
|
class |
Version
This class contains the version number of the current WEKA release and some
methods for comparing another version string.
|
Modifier and Type | Method and Description |
---|---|
static String |
RevisionUtils.extract(RevisionHandler handler)
Extracts the revision string returned by the RevisionHandler.
|
static RevisionUtils.Type |
RevisionUtils.getType(RevisionHandler handler)
Determines the type of a (sanitized) revision string returned by the
RevisionHandler.
|
Modifier and Type | Interface and Description |
---|---|
interface |
Loader
Interface to something that can load Instances from an input source in some
format.
|
interface |
Saver
Interface to something that can save Instances to an output destination in some
format.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractFileLoader
Abstract superclass for all file loaders.
|
class |
AbstractFileSaver
Abstract class for Savers that save to a file
Valid options are:
-i input arff file
The input filw in arff format. |
class |
AbstractLoader
Abstract class gives default implementation of setSource
methods.
|
class |
AbstractSaver
Abstract class for Saver
|
class |
ArffLoader
Reads a source that is in arff (attribute relation
file format) format.
|
static class |
ArffLoader.ArffReader
Reads data from an ARFF file, either in incremental or batch mode.
|
class |
ArffSaver
Writes to a destination in arff text format.
|
class |
C45Loader
Reads a file that is C45 format.
|
class |
C45Saver
Writes to a destination that is in the format used
by the C4.5 algorithm.
Therefore it outputs a names and a data file. |
class |
ConverterUtils
Utility routines for the converter package.
|
static class |
ConverterUtils.DataSink
Helper class for saving data to files.
|
static class |
ConverterUtils.DataSource
Helper class for loading data from files and URLs.
|
class |
CSVLoader
Reads a source that is in comma separated or tab
separated format.
|
class |
CSVSaver
Writes to a destination that is in csv format
Valid options are:
|
class |
DatabaseConnection
Connects to a database.
|
class |
DatabaseLoader
Reads Instances from a Database.
|
class |
DatabaseSaver
Writes to a database (tested with MySQL, InstantDB, HSQLDB).
|
class |
LibSVMLoader
Reads a source that is in libsvm format.
For more information about libsvm see: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ |
class |
LibSVMSaver
Writes to a destination that is in libsvm format.
For more information about libsvm see: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Valid options are: |
class |
SerializedInstancesLoader
Reads a source that contains serialized Instances.
|
class |
SerializedInstancesSaver
Serializes the instances to a file with extension bsi.
|
class |
SVMLightLoader
Reads a source that is in svm light format.
For more information about svm light see: http://svmlight.joachims.org/ |
class |
SVMLightSaver
Writes to a destination that is in svm light format.
For more information about svm light see: http://svmlight.joachims.org/ Valid options are: |
class |
TextDirectoryLoader
Loads all text files in a directory and uses the subdirectory names as class labels.
|
class |
XRFFLoader
Reads a source that is in the XML version of the ARFF format.
|
class |
XRFFSaver
Writes to a destination that is in the XML version of the ARFF format.
|
Modifier and Type | Class and Description |
---|---|
class |
ConsoleLogger
A simple logger that outputs the logging information in the console.
|
class |
FileLogger
A simple file logger, that just logs to a single file.
|
class |
Logger
Abstract superclass for all loggers.
|
class |
OutputLogger
A logger that logs all output on stdout and stderr to a file.
|
Modifier and Type | Class and Description |
---|---|
class |
CholeskyDecomposition
Cholesky Decomposition.
|
class |
DoubleVector
A vector specialized on doubles.
|
class |
EigenvalueDecomposition
Eigenvalues and eigenvectors of a real matrix.
|
class |
ExponentialFormat |
class |
FlexibleDecimalFormat |
class |
FloatingPointFormat
Class for the format of floating point numbers
|
class |
IntVector
A vector specialized on integers.
|
class |
LinearRegression
Class for performing (ridged) linear regression using Tikhonov
regularization.
|
class |
LUDecomposition
LU Decomposition.
|
class |
Maths
Utility class.
|
class |
QRDecomposition
QR Decomposition.
|
class |
SingularValueDecomposition
Singular Value Decomposition.
|
Modifier and Type | Class and Description |
---|---|
class |
BallTree
Class implementing the BallTree/Metric Tree algorithm for nearest neighbour search.
The connection to dataset is only a reference. |
class |
CoverTree
Class implementing the CoverTree datastructure.
The class is very much a translation of the c source code made available by the authors. For more information and original source code see: Alina Beygelzimer, Sham Kakade, John Langford: Cover trees for nearest neighbor. |
class |
CoverTree.CoverTreeNode
class representing a node of the cover tree.
|
class |
KDTree
Class implementing the KDTree search algorithm for nearest neighbour search.
The connection to dataset is only a reference. |
class |
LinearNNSearch
Class implementing the brute force search algorithm for nearest neighbour search.
|
class |
NearestNeighbourSearch
Abstract class for nearest neighbour search.
|
class |
PerformanceStats
The class that measures the performance of a nearest
neighbour search (NNS) algorithm.
|
class |
TreePerformanceStats
The class that measures the performance of a tree based
nearest neighbour search algorithm.
|
Modifier and Type | Class and Description |
---|---|
class |
BallNode
Class representing a node of a BallTree.
|
class |
BallSplitter
Abstract class for splitting a ball tree's BallNode.
|
class |
BallTreeConstructor
Abstract class for constructing a BallTree .
|
class |
BottomUpConstructor
The class that constructs a ball tree bottom up.
|
class |
MedianDistanceFromArbitraryPoint
Class that splits a BallNode of a ball tree using Uhlmann's described method.
For information see: Jeffrey K. |
class |
MiddleOutConstructor
The class that builds a BallTree middle out.
For more information see also: Andrew W. |
class |
PointsClosestToFurthestChildren
Implements the Moore's method to split a node of a ball tree.
For more information please see section 2 of the 1st and 3.2.3 of the 2nd: Andrew W. |
class |
TopDownConstructor
The class implementing the TopDown construction method of ball trees.
|
Modifier and Type | Class and Description |
---|---|
class |
Stack<T>
Class implementing a stack.
|
Modifier and Type | Class and Description |
---|---|
class |
KDTreeNode
A class representing a KDTree node.
|
class |
KDTreeNodeSplitter
Class that splits up a KDTreeNode.
|
class |
KMeansInpiredMethod
The class that splits a node into two such that the overall sum of squared distances of points to their centres on both sides of the (axis-parallel) splitting plane is minimum.
For more information see also: Ashraf Masood Kibriya (2007). |
class |
MedianOfWidestDimension
The class that splits a KDTree node based on the median value of a dimension in which the node's points have the widest spread.
For more information see also: Jerome H. |
class |
MidPointOfWidestDimension
The class that splits a KDTree node based on the midpoint value of a dimension in which the node's points have the widest spread.
For more information see also: Andrew Moore (1991). |
class |
SlidingMidPointOfWidestSide
The class that splits a node into two based on the midpoint value of the dimension in which the node's rectangle is widest.
|
Modifier and Type | Interface and Description |
---|---|
interface |
Stemmer
Interface for all stemming algorithms.
|
Modifier and Type | Class and Description |
---|---|
class |
IteratedLovinsStemmer
An iterated version of the Lovins stemmer.
|
class |
LovinsStemmer
A stemmer based on the Lovins stemmer, described here:
Julie Beth Lovins (1968). |
class |
NullStemmer
A dummy stemmer that performs no stemming at all.
|
class |
SnowballStemmer
A wrapper class for the Snowball stemmers.
|
class |
Stemming
A helper class for using the stemmers.
|
Modifier and Type | Class and Description |
---|---|
class |
AlphabeticTokenizer
Alphabetic string tokenizer, tokens are to be formed only from contiguous alphabetic sequences.
|
class |
CharacterDelimitedTokenizer
Abstract superclass for tokenizers that take characters as delimiters.
|
class |
NGramTokenizer
Splits a string into an n-gram with min and max
grams.
|
class |
Tokenizer
A superclass for all tokenizer algorithms.
|
class |
WordTokenizer
A simple tokenizer that is using the java.util.StringTokenizer class to tokenize the strings.
|
Modifier and Type | Class and Description |
---|---|
class |
KOML
This class is a helper class for XML serialization using KOML .
|
class |
MethodHandler
This class handles relationships between display names of properties
(or classes) and Methods that are associated with them.
|
class |
PropertyHandler
This class stores information about properties to ignore or properties
that are allowed for a certain class.
|
class |
SerialUIDChanger
This class enables one to change the UID of a serialized object and therefore
not losing the data stored in the binary format.
|
class |
XMLBasicSerialization
This serializer contains some read/write methods for common classes that
are not beans-conform.
|
class |
XMLDocument
This class offers some methods for generating, reading and writing
XML documents.
It can only handle UTF-8. |
class |
XMLInstances
XML representation of the Instances class.
|
class |
XMLOptions
A class for transforming options listed in XML to a regular WEKA command
line string.
|
class |
XMLSerialization
With this class objects can be serialized to XML instead into a binary
format.
|
class |
XMLSerializationMethodHandler
This class handles relationships between display names of properties
(or classes) and Methods that are associated with them.
|
class |
XStream
This class is a helper class for XML serialization using
XStream .
|
Modifier and Type | Class and Description |
---|---|
class |
ClassificationGenerator
Abstract class for data generators for classifiers.
|
class |
ClusterDefinition
Ancestor to all ClusterDefinitions, i.e., subclasses that handle their
own parameters that the cluster generator only passes on.
|
class |
ClusterGenerator
Abstract class for cluster data generators.
|
class |
DataGenerator
Abstract superclass for data generators that generate data for classifiers
and clusterers.
|
class |
RegressionGenerator
Abstract class for data generators for regression classifiers.
|
class |
Test
Class to represent a test.
|
Modifier and Type | Class and Description |
---|---|
class |
Agrawal
Generates a people database and is based on the paper by Agrawal et al.:
R. |
class |
BayesNet
Generates random instances based on a Bayes network.
|
class |
LED24
This generator produces data for a display with 7 LEDs.
|
class |
RandomRBF
RandomRBF data is generated by first creating a random set of centers for each class.
|
class |
RDG1
A data generator that produces data randomly by producing a decision list.
The decision list consists of rules. Instances are generated randomly one by one. |
Modifier and Type | Class and Description |
---|---|
class |
Expression
A data generator for generating y according to a given expression out of randomly generated x.
E.g., the mexican hat can be generated like this: sin(abs(a1)) / abs(a1) In addition to this function, the amplitude can be changed and gaussian noise can be added. |
class |
MexicanHat
A data generator for the simple 'Mexian Hat' function:
y = sin|x| / |x| In addition to this simple function, the amplitude can be changed and gaussian noise can be added. |
Modifier and Type | Class and Description |
---|---|
class |
BIRCHCluster
Cluster data generator designed for the BIRCH System
Dataset is generated with instances in K clusters. Instances are 2-d data points. Each cluster is characterized by the number of data points in itits radius and its center. |
class |
SubspaceCluster
A data generator that produces data points in hyperrectangular subspace clusters.
|
class |
SubspaceClusterDefinition
A single cluster for the SubspaceCluster
datagenerator
Valid options are:
|
Modifier and Type | Interface and Description |
---|---|
interface |
ConditionalEstimator
Interface for conditional probability estimators.
|
Modifier and Type | Class and Description |
---|---|
class |
CheckEstimator
Class for examining the capabilities and finding problems with
estimators.
|
static class |
CheckEstimator.AttrTypes
class that contains info about the attribute types the estimator can estimate
estimator work on one attribute only
|
static class |
CheckEstimator.EstTypes
public class that contains info about the chosen attribute type
estimator work on one attribute only
|
class |
CheckEstimator.PostProcessor
a class for postprocessing the test-data
|
class |
DDConditionalEstimator
Conditional probability estimator for a discrete domain conditional upon
a discrete domain.
|
class |
DiscreteEstimator
Simple symbolic probability estimator based on symbol counts.
|
class |
DKConditionalEstimator
Conditional probability estimator for a discrete domain conditional upon
a numeric domain.
|
class |
DNConditionalEstimator
Conditional probability estimator for a discrete domain conditional upon
a numeric domain.
|
class |
Estimator
Abstract class for all estimators.
|
class |
EstimatorUtils
Contains static utility functions for Estimators.
|
class |
KDConditionalEstimator
Conditional probability estimator for a numeric domain conditional upon
a discrete domain (utilises separate kernel estimators for each discrete
conditioning value).
|
class |
KernelEstimator
Simple kernel density estimator.
|
class |
KKConditionalEstimator
Conditional probability estimator for a numeric domain conditional upon
a numeric domain.
|
class |
MahalanobisEstimator
Simple probability estimator that places a single normal distribution
over the observed values.
|
class |
NDConditionalEstimator
Conditional probability estimator for a numeric domain conditional upon
a discrete domain (utilises separate normal estimators for each discrete
conditioning value).
|
class |
NNConditionalEstimator
Conditional probability estimator for a numeric domain conditional upon
a numeric domain (using Mahalanobis distance).
|
class |
NormalEstimator
Simple probability estimator that places a single normal distribution
over the observed values.
|
class |
PoissonEstimator
Simple probability estimator that places a single Poisson distribution
over the observed values.
|
Modifier and Type | Class and Description |
---|---|
class |
AveragingResultProducer
Takes the results from a ResultProducer and submits
the average to the result listener.
|
class |
ClassifierSplitEvaluator
A SplitEvaluator that produces results for a
classification scheme on a nominal class attribute.
|
class |
CostSensitiveClassifierSplitEvaluator
SplitEvaluator that produces results for a classification scheme on a nominal class attribute, including weighted misclassification costs.
|
class |
CrossValidationResultProducer
Generates for each run, carries out an n-fold
cross-validation, using the set SplitEvaluator to generate some results.
|
class |
CSVResultListener
Takes results from a result producer and assembles them into comma separated value form.
|
class |
DatabaseResultListener
Takes results from a result producer and sends them to a database.
|
class |
DatabaseResultProducer
Examines a database and extracts out the results
produced by the specified ResultProducer and submits them to the specified
ResultListener.
|
class |
DatabaseUtils
DatabaseUtils provides utility functions for accessing the experiment
database.
|
class |
DensityBasedClustererSplitEvaluator
A SplitEvaluator that produces results for a density based clusterer.
|
class |
Experiment
Holds all the necessary configuration information for a standard
type experiment.
|
class |
InstanceQuery
Convert the results of a database query into instances.
|
class |
InstancesResultListener
Outputs the received results in arff format to a Writer.
|
class |
LearningRateResultProducer
Tells a sub-ResultProducer to reproduce the current
run for varying sized subsamples of the dataset.
|
class |
OutputZipper
OutputZipper writes output to either gzipped files or to a
multi entry zip file.
|
class |
PairedCorrectedTTester
Behaves the same as PairedTTester, only it uses the corrected
resampled t-test statistic.
For more information see:
Claude Nadeau, Yoshua Bengio (2001).
|
class |
PairedStats
A class for storing stats on a paired comparison (t-test and correlation)
|
class |
PairedStatsCorrected
A class for storing stats on a paired comparison.
|
class |
PairedTTester
Calculates T-Test statistics on data stored in a set of instances.
|
class |
PropertyNode
Stores information on a property of an object: the class of the
object with the property; the property descriptor, and the current
value.
|
class |
RandomSplitResultProducer
Generates a single train/test split and calls the
appropriate SplitEvaluator to generate some results.
|
class |
RegressionSplitEvaluator
A SplitEvaluator that produces results for a
classification scheme on a numeric class attribute.
|
class |
RemoteEngine
A general purpose server for executing Task objects sent via RMI.
|
class |
RemoteExperiment
Holds all the necessary configuration information for a distributed
experiment.
|
class |
RemoteExperimentSubTask
Class to encapsulate an experiment as a task that can be executed on
a remote host.
|
class |
ResultMatrix
This matrix is a container for the datasets and classifier setups and
their statistics.
|
class |
ResultMatrixCSV
This matrix is a container for the datasets and classifier setups and
their statistics.
|
class |
ResultMatrixGnuPlot
This matrix is a container for the datasets and classifier setups and
their statistics.
|
class |
ResultMatrixHTML
This matrix is a container for the datasets and classifier setups and
their statistics.
|
class |
ResultMatrixLatex
This matrix is a container for the datasets and classifier setups and
their statistics.
|
class |
ResultMatrixPlainText
This matrix is a container for the datasets and classifier setups and
their statistics.
|
class |
ResultMatrixSignificance
This matrix is a container for the datasets and classifier setups and
their statistics.
|
class |
Stats
A class to store simple statistics
|
class |
TaskStatusInfo
A class holding information for tasks being executed
on RemoteEngines.
|
Modifier and Type | Class and Description |
---|---|
class |
XMLExperiment
This class serializes and deserializes an Experiment instance to and
fro XML.
It omits the options from the Experiment, since these are handled
by the get/set-methods. |
Modifier and Type | Class and Description |
---|---|
class |
AllFilter
A simple instance filter that passes all instances directly
through.
|
class |
CheckSource
A simple class for checking the source generated from Filters
implementing the
weka.filters.Sourcable interface. |
class |
Filter
An abstract class for instance filters: objects that take instances
as input, carry out some transformation on the instance and then
output the instance.
|
class |
MultiFilter
Applies several filters successively.
|
class |
SimpleBatchFilter
This filter is a superclass for simple batch filters.
|
class |
SimpleFilter
This filter contains common behavior of the SimpleBatchFilter and the
SimpleStreamFilter.
|
class |
SimpleStreamFilter
This filter is a superclass for simple stream filters.
|
Modifier and Type | Class and Description |
---|---|
class |
AddClassification
A filter for adding the classification, the class distribution and an error flag to a dataset with a classifier.
|
class |
ClassOrder
Changes the order of the classes so that the class values are no longer of in the order specified in the header.
|
class |
Discretize
An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
|
class |
NominalToBinary
Converts all nominal attributes into binary numeric attributes.
|
class |
PLSFilter
Runs Partial Least Square Regression over the given instances and computes the resulting beta matrix for prediction.
By default it replaces missing values and centers the data. For more information see: Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002). |
Modifier and Type | Class and Description |
---|---|
class |
Resample
Produces a random subsample of a dataset using either sampling with replacement or without replacement.
The original dataset must fit entirely in memory. |
class |
SMOTE
Resamples a dataset by applying the Synthetic
Minority Oversampling TEchnique (SMOTE).
|
class |
SpreadSubsample
Produces a random subsample of a dataset.
|
class |
StratifiedRemoveFolds
This filter takes a dataset and outputs a specified fold for cross validation.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractTimeSeries
An abstract instance filter that assumes instances form time-series data and
performs some merging of attribute values in the current instance with
attribute attribute values of some previous (or future) instance.
|
class |
Add
An instance filter that adds a new attribute to the dataset.
|
class |
AddCluster
A filter that adds a new nominal attribute representing the cluster assigned to each instance by the specified clustering algorithm.
|
class |
AddExpression
An instance filter that creates a new attribute by applying a mathematical expression to existing attributes.
|
class |
AddID
An instance filter that adds an ID attribute to the dataset.
|
class |
AddNoise
An instance filter that changes a percentage of a given attributes values.
|
class |
AddValues
Adds the labels from the given list to an attribute if they are missing.
|
class |
Center
Centers all numeric attributes in the given dataset to have zero mean (apart from the class attribute, if set).
|
class |
ChangeDateFormat
Changes the date format used by a date attribute.
|
class |
ClassAssigner
Filter that can set and unset the class index.
|
class |
ClusterMembership
A filter that uses a density-based clusterer to generate cluster membership values; filtered instances are composed of these values plus the class attribute (if set in the input data).
|
class |
Copy
An instance filter that copies a range of attributes in the dataset.
|
class |
FirstOrder
This instance filter takes a range of N numeric attributes and replaces them with N-1 numeric attributes, the values of which are the difference between consecutive attribute values from the original instance.
|
class |
InterquartileRange
A filter for detecting outliers and extreme values based on interquartile ranges.
|
class |
KernelFilter
Converts the given set of predictor variables into a kernel matrix.
|
class |
MakeIndicator
A filter that creates a new dataset with a boolean attribute replacing a nominal attribute.
|
class |
MathExpression
Modify numeric attributes according to a given expression
Valid options are:
|
class |
MergeTwoValues
Merges two values of a nominal attribute into one value.
|
class |
MultiInstanceToPropositional
Converts the multi-instance dataset into single instance dataset so that the Nominalize, Standardize and other type of filters or transformation can be applied to these data for the further preprocessing.
Note: the first attribute of the converted dataset is a nominal attribute and refers to the bagId. |
class |
NominalToString
Converts a nominal attribute (i.e.
|
class |
Normalize
Normalizes all numeric values in the given dataset (apart from the class attribute, if set).
|
class |
NumericCleaner
A filter that 'cleanses' the numeric data from values that are too small, too big or very close to a certain value (e.g., 0) and sets these values to a pre-defined default.
|
class |
NumericToBinary
Converts all numeric attributes into binary attributes (apart from the class attribute, if set): if the value of the numeric attribute is exactly zero, the value of the new attribute will be zero.
|
class |
NumericToNominal
A filter for turning numeric attributes into
nominal ones.
|
class |
NumericTransform
Transforms numeric attributes using a given transformation method.
|
class |
Obfuscate
A simple instance filter that renames the relation, all attribute names and all nominal (and string) attribute values.
|
class |
PartitionedMultiFilter
A filter that applies filters on subsets of attributes and assembles the output into a new dataset.
|
class |
PKIDiscretize
Discretizes numeric attributes using equal frequency binning, where the number of bins is equal to the square root of the number of non-missing values.
For more information, see: Ying Yang, Geoffrey I. |
class |
PotentialClassIgnorer
This filter should be extended by other unsupervised attribute
filters to allow processing of the class attribute if that's
required.
|
class |
PropositionalToMultiInstance
Converts a propositional dataset into a multi-instance dataset (with relational attribute).
|
class |
RandomProjection
Reduces the dimensionality of the data by
projecting it onto a lower dimensional subspace using a random matrix with
columns of unit length (i.e.
|
class |
RandomSubset
Chooses a random subset of attributes, either an absolute number or a percentage.
|
class |
RELAGGS
A propositionalization filter inspired by the RELAGGS algorithm.
It processes all relational attributes that fall into the user defined range (all others are skipped, i.e., not added to the output). |
class |
Remove
A filter that removes a range of attributes from the dataset.
|
class |
RemoveType
Removes attributes of a given type.
|
class |
RemoveUseless
This filter removes attributes that do not vary at all or that vary too much.
|
class |
Reorder
A filter that generates output with a new order of the attributes.
|
class |
ReplaceMissingValues
Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
|
class |
Standardize
Standardizes all numeric attributes in the given dataset to have zero mean and unit variance (apart from the class attribute, if set).
|
class |
StringToNominal
Converts a string attribute (i.e.
|
class |
StringToWordVector
Converts String attributes into a set of attributes representing word occurrence (depending on the tokenizer) information from the text contained in the strings.
|
class |
SwapValues
Swaps two values of a nominal attribute.
|
class |
TimeSeriesDelta
An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the difference between the current value and the equivalent attribute attribute value of some previous (or future) instance.
|
class |
TimeSeriesTranslate
An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the equivalent attribute values of some previous (or future) instance.
|
class |
Wavelet
A filter for wavelet transformation.
For more information see: Wikipedia (2004). |
Modifier and Type | Class and Description |
---|---|
class |
NonSparseToSparse
An instance filter that converts all incoming instances into sparse format.
|
class |
Randomize
Randomly shuffles the order of instances passed through it.
|
class |
RemoveFolds
This filter takes a dataset and outputs a specified fold for cross validation.
|
class |
RemoveFrequentValues
Determines which values (frequent or infrequent ones) of an (nominal) attribute are retained and filters the instances accordingly.
|
class |
RemoveMisclassified
A filter that removes instances which are incorrectly classified.
|
class |
RemovePercentage
A filter that removes a given percentage of a dataset.
|
class |
RemoveRange
A filter that removes a given range of instances of a dataset.
|
class |
RemoveWithValues
Filters instances according to the value of an attribute.
|
class |
ReservoirSample
Produces a random subsample of a dataset using the reservoir sampling Algorithm "R" by Vitter.
|
class |
SparseToNonSparse
An instance filter that converts all incoming sparse instances into non-sparse format.
|
class |
SubsetByExpression
Filters instances according to a user-specified expression.
Grammar: boolexpr_list ::= boolexpr_list boolexpr_part | boolexpr_part; boolexpr_part ::= boolexpr:e {: parser.setResult(e); :} ; boolexpr ::= BOOLEAN | true | false | expr < expr | expr <= expr | expr > expr | expr >= expr | expr = expr | ( boolexpr ) | not boolexpr | boolexpr and boolexpr | boolexpr or boolexpr | ATTRIBUTE is STRING ; expr ::= NUMBER | ATTRIBUTE | ( expr ) | opexpr | funcexpr ; opexpr ::= expr + expr | expr - expr | expr * expr | expr / expr ; funcexpr ::= abs ( expr ) | sqrt ( expr ) | log ( expr ) | exp ( expr ) | sin ( expr ) | cos ( expr ) | tan ( expr ) | rint ( expr ) | floor ( expr ) | pow ( expr for base , expr for exponent ) | ceil ( expr ) ; Notes: - NUMBER any integer or floating point number (but not in scientific notation!) - STRING any string surrounded by single quotes; the string may not contain a single quote though. - ATTRIBUTE the following placeholders are recognized for attribute values: - CLASS for the class value in case a class attribute is set. - ATTxyz with xyz a number from 1 to # of attributes in the dataset, representing the value of indexed attribute. Examples: - extracting only mammals and birds from the 'zoo' UCI dataset: (CLASS is 'mammal') or (CLASS is 'bird') - extracting only animals with at least 2 legs from the 'zoo' UCI dataset: (ATT14 >= 2) - extracting only instances with non-missing 'wage-increase-second-year' from the 'labor' UCI dataset: not ismissing(ATT3) Valid options are: |
Modifier and Type | Class and Description |
---|---|
class |
FlowRunner
Small utility class for executing KnowledgeFlow
flows outside of the KnowledgeFlow application
|
Modifier and Type | Class and Description |
---|---|
class |
XMLBeans
This class serializes and deserializes a KnowledgeFlow setup to and fro XML.
|
Modifier and Type | Class and Description |
---|---|
class |
DbUtils
A little bit extended DatabaseUtils class.
|
Copyright © 2019 University of Waikato, Hamilton, NZ. All rights reserved.