Modifier and Type | Class and Description |
---|---|
class |
Apriori
Class implementing an Apriori-type algorithm.
|
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 |
Tertius
Finds rules according to confirmation measure (Tertius-type algorithm).
For more information see: P. |
Modifier and Type | Class and Description |
---|---|
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 |
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 |
GeneticSearch
GeneticSearch:
Performs a search using the simple genetic algorithm described in Goldberg (1989). For more information see: David E. |
class |
LinearForwardSelection
LinearForwardSelection:
Extension of BestFirst. |
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 |
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 |
SVMAttributeEval
SVMAttributeEval :
Evaluates the worth of an attribute by using an SVM classifier. |
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. |
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 |
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 |
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). |
Modifier and Type | Class and Description |
---|---|
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 |
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 |
GaussianProcesses
Implements Gaussian Processes for regression without hyperparameter-tuning.
|
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 |
PaceRegression
Class for building pace regression linear models and using them for prediction.
|
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 |
ChisqMixture
Class for manipulating chi-square mixture distributions.
|
class |
MixtureDistribution
Abtract class for manipulating mixture distributions.
|
class |
NormalMixture
Class for manipulating normal mixture distributions.
|
Modifier and Type | Class and Description |
---|---|
class |
Puk
The Pearson VII function-based universal kernel.
For more information see: B. |
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 |
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 |
Bagging
Class for bagging a classifier to reduce variance.
|
class |
ClassificationViaRegression
Class for doing classification using regression methods.
|
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 |
Grading
Implements Grading.
|
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 |
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 |
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 |
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 |
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 |
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. |
Modifier and Type | Class and Description |
---|---|
class |
VFI
Classification by voting feature intervals.
|
Modifier and Type | Class and Description |
---|---|
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.
|
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 |
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 |
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 |
M5Base
M5Base.
|
Modifier and Type | Class and Description |
---|---|
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 |
FarthestFirst
Cluster data using the FarthestFirst algorithm.
For more information see: Hochbaum, Shmoys (1985). |
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 |
sIB
Cluster data using the sequential information bottleneck algorithm.
Note: only hard clustering scheme is supported. |
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 |
ChebyshevDistance
Implements the Chebyshev distance.
|
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 |
ManhattanDistance
Implements the Manhattan distance (or Taxicab geometry).
|
class |
Optimization
Implementation of Active-sets method with BFGS update to solve optimization
problem with only bounds constraints in multi-dimensions.
|
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 |
KDTree
Class implementing the KDTree search algorithm for nearest neighbour search.
The connection to dataset is only a reference. |
Modifier and Type | Class and Description |
---|---|
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 |
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 | 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). |
Modifier and Type | Class and Description |
---|---|
class |
Agrawal
Generates a people database and is based on the paper by Agrawal et al.:
R. |
class |
LED24
This generator produces data for a display with 7 LEDs.
|
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. |
Modifier and Type | Class and Description |
---|---|
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).
|
Modifier and Type | Class and Description |
---|---|
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 |
SMOTE
Resamples a dataset by applying the Synthetic
Minority Oversampling TEchnique (SMOTE).
|
Modifier and Type | Class and Description |
---|---|
class |
KernelFilter
Converts the given set of predictor variables into a kernel matrix.
|
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 |
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 |
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 |
Wavelet
A filter for wavelet transformation.
For more information see: Wikipedia (2004). |
Modifier and Type | Class and Description |
---|---|
class |
BoundaryVisualizer
BoundaryVisualizer.
|
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