Modifier and Type | Class and Description |
---|---|
class |
AbstractAssociator
Abstract scheme for learning associations.
|
class |
Apriori
Class implementing an Apriori-type algorithm.
|
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 |
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 |
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 |
ASEvaluation
Abstract attribute selection evaluation class
|
class |
AttributeSetEvaluator
Abstract attribute set evaluator.
|
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
Classifier
Abstract classifier.
|
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 |
BayesNet
Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier. |
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 |
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. |
Modifier and Type | Class and Description |
---|---|
class |
DiscreteEstimatorBayes
Symbolic probability estimator based on symbol counts and a prior.
|
class |
DiscreteEstimatorFullBayes
Symbolic probability estimator based on symbol counts and a prior.
|
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 |
LinearRegression
Class for using linear regression for prediction.
|
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 |
CachedKernel
Base class for RBFKernel and PolyKernel that implements a simple LRU.
|
class |
Kernel
Abstract kernel.
|
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 |
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 |
LWL
Locally weighted learning.
|
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 |
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 |
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 |
ZeroR
Class for building and using a 0-R classifier.
|
Modifier and Type | Class and Description |
---|---|
class |
MakeDecList
Class for handling a decision list.
|
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 |
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 |
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 |
ClassifierTree
Class for handling a tree structure used for
classification.
|
class |
NBTreeClassifierTree
Class for handling a naive bayes tree structure used for
classification.
|
class |
PruneableClassifierTree
Class for handling a tree structure that can
be pruned using a pruning set.
|
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.
|
Modifier and Type | Class and Description |
---|---|
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
|
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 |
CLOPE
Yiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional data.
|
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 | Interface and Description |
---|---|
interface |
MultiInstanceCapabilitiesHandler
Multi-Instance classifiers can specify an additional Capabilities object
for the data in the relational attribute, since the format of multi-instance
data is fixed to "bag/NOMINAL,data/RELATIONAL,class".
|
Modifier and Type | Class and Description |
---|---|
class |
FindWithCapabilities
Locates all classes with certain capabilities.
|
Modifier and Type | Method and Description |
---|---|
CapabilitiesHandler |
TestInstances.getHandler()
returns the current set CapabilitiesHandler to generate the dataset
for, can be null
|
CapabilitiesHandler |
FindWithCapabilities.getHandler()
returns the current set CapabilitiesHandler to generate the dataset
for, can be null.
|
CapabilitiesHandler |
Capabilities.getOwner()
returns the owner of this capabilities object
|
Modifier and Type | Method and Description |
---|---|
void |
TestInstances.setHandler(CapabilitiesHandler value)
sets the Capabilities handler to generate the data for
|
void |
FindWithCapabilities.setHandler(CapabilitiesHandler value)
sets the Capabilities handler to generate the data for.
|
void |
Capabilities.setOwner(CapabilitiesHandler value)
sets the owner of this capabilities object
|
Constructor and Description |
---|
Capabilities(CapabilitiesHandler owner)
initializes the capabilities for the given owner
|
Modifier and Type | Class and Description |
---|---|
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 |
AbstractSaver
Abstract class for Saver
|
class |
ArffSaver
Writes to a destination in arff text 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 |
CSVSaver
Writes to a destination that is in csv format
Valid options are:
|
class |
DatabaseSaver
Writes to a database (tested with MySQL, InstantDB, HSQLDB).
|
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 |
SerializedInstancesSaver
Serializes the instances to a file with extension bsi.
|
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 |
XRFFSaver
Writes to a destination that is in the XML version of the ARFF format.
|
Modifier and Type | Class and Description |
---|---|
class |
DiscreteEstimator
Simple symbolic probability estimator based on symbol counts.
|
class |
Estimator
Abstract class for all estimators.
|
class |
KernelEstimator
Simple kernel density estimator.
|
class |
MahalanobisEstimator
Simple probability estimator that places a single normal distribution
over the observed values.
|
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 |
AllFilter
A simple instance filter that passes all instances directly
through.
|
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 |
AttributeSelection
A supervised attribute filter that can be used to select attributes.
|
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: |
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