Uses of Class
weka.classifiers.Classifier

Packages that use Classifier

weka.attributeSelection
weka.classifiers
weka.classifiers.bayes
weka.classifiers.evaluation
weka.classifiers.functions
weka.classifiers.lazy
weka.classifiers.meta
weka.classifiers.misc
weka.classifiers.rules
weka.classifiers.trees
weka.classifiers.trees.lmt
weka.classifiers.trees.m5
weka.experiment
weka.filters.unsupervised.instance
weka.gui.beans
weka.gui.boundaryvisualizer
weka.gui.experiment
weka.gui.explorer

Uses of Classifier in weka.attributeSelection

Fields in weka.attributeSelection declared as Classifier

private Classifier	WrapperSubsetEval.m_BaseClassifier holds the base classifier object
private Classifier	ClassifierSubsetEval.m_Classifier holds the classifier to use for error estimates

Methods in weka.attributeSelection that return Classifier

Classifier	WrapperSubsetEval.getClassifier() Get the classifier used as the base learner.
Classifier	ClassifierSubsetEval.getClassifier() Get the classifier used as the base learner.

Methods in weka.attributeSelection with parameters of type Classifier

void	WrapperSubsetEval.setClassifier(Classifier newClassifier) Set the classifier to use for accuracy estimation
void	ClassifierSubsetEval.setClassifier(Classifier newClassifier) Set the classifier to use for accuracy estimation

Uses of Classifier in weka.classifiers

Subclasses of Classifier in weka.classifiers

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.

Fields in weka.classifiers declared as Classifier

protected Classifier	CheckClassifier.m_Classifier The classifier to be examined
protected Classifier[]	IteratedSingleClassifierEnhancer.m_Classifiers Array for storing the generated base classifiers.
protected Classifier[]	MultipleClassifiersCombiner.m_Classifiers Array for storing the generated base classifiers.
protected Classifier	BVDecompose.m_Classifier An instantiated base classifier used for getting and testing options.
protected Classifier	BVDecomposeSegCVSub.m_Classifier An instantiated base classifier used for getting and testing options.
protected Classifier	SingleClassifierEnhancer.m_Classifier The base classifier to use

Methods in weka.classifiers that return Classifier

Classifier	CheckClassifier.getClassifier() Get the classifier used as the classifier
static Classifier	Classifier.forName(java.lang.String classifierName, java.lang.String[] options) Creates a new instance of a classifier given it's class name and (optional) arguments to pass to it's setOptions method.
static Classifier[]	Classifier.makeCopies(Classifier model, int num) Creates copies of the current classifier, which can then be used for boosting etc.
Classifier[]	MultipleClassifiersCombiner.getClassifiers() Gets the list of possible classifers to choose from.
Classifier	MultipleClassifiersCombiner.getClassifier(int index) Gets a single classifier from the set of available classifiers.
Classifier	BVDecompose.getClassifier() Gets the name of the classifier being analysed
Classifier	BVDecomposeSegCVSub.getClassifier() Gets the name of the classifier being analysed
Classifier	SingleClassifierEnhancer.getClassifier() Get the classifier used as the base learner.

Methods in weka.classifiers with parameters of type Classifier

void	CheckClassifier.setClassifier(Classifier newClassifier) Set the classifier for boosting.
protected boolean	CheckClassifier.testWRTZeroR(Classifier classifier, Evaluation evaluation, Instances train, Instances test) Determine whether the scheme performs worse than ZeroR during testing
static Classifier[]	Classifier.makeCopies(Classifier model, int num) Creates copies of the current classifier, which can then be used for boosting etc.
void	MultipleClassifiersCombiner.setClassifiers(Classifier[] classifiers) Sets the list of possible classifers to choose from.
void	BVDecompose.setClassifier(Classifier newClassifier) Set the classifiers being analysed
void	BVDecomposeSegCVSub.setClassifier(Classifier newClassifier) Set the classifiers being analysed
void	Evaluation.crossValidateModel(Classifier classifier, Instances data, int numFolds, java.util.Random random) Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
static java.lang.String	Evaluation.evaluateModel(Classifier classifier, java.lang.String[] options) Evaluates a classifier with the options given in an array of strings.
void	Evaluation.evaluateModel(Classifier classifier, Instances data) Evaluates the classifier on a given set of instances.
double	Evaluation.evaluateModelOnce(Classifier classifier, Instance instance) Evaluates the classifier on a single instance.
protected static java.lang.String	Evaluation.printClassifications(Classifier classifier, Instances train, java.lang.String testFileName, int classIndex, Range attributesToOutput) Prints the predictions for the given dataset into a String variable.
protected static java.lang.String	Evaluation.makeOptionString(Classifier classifier) Make up the help string giving all the command line options
void	SingleClassifierEnhancer.setClassifier(Classifier newClassifier) Set the base learner.

Uses of Classifier in weka.classifiers.bayes

Subclasses of Classifier in weka.classifiers.bayes

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	BayesNet Base class for a Bayes Network classifier.
class	BayesNetB Class for a Bayes Network classifier based on a hill climbing algorithm for learning structure as described in Buntine, W. (1991).
class	BayesNetB2 Class for a Bayes Network classifier based on Buntines hill climbing algorithm for learning structure, but augmented to allow arc reversal as an operation.
class	BayesNetK2 Class for a Bayes Network classifier based on K2 for learning structure.
class	ComplementNaiveBayes Class for building and using a Complement class Naive Bayes classifier.
class	NaiveBayes Class for a Naive Bayes classifier using estimator classes.
class	NaiveBayesMultinomial The core equation for this classifier: P[Ci|D] = (P[D|Ci] x P[Ci]) / P[D] (Bayes rule) where Ci is class i and D is a document
class	NaiveBayesSimple Class for building and using a simple Naive Bayes classifier.
class	NaiveBayesUpdateable Class for a Naive Bayes classifier using estimator classes.

Uses of Classifier in weka.classifiers.evaluation

Methods in weka.classifiers.evaluation with parameters of type Classifier

FastVector	EvaluationUtils.getCVPredictions(Classifier classifier, Instances data, int numFolds) Generate a bunch of predictions ready for processing, by performing a cross-validation on the supplied dataset.
FastVector	EvaluationUtils.getTrainTestPredictions(Classifier classifier, Instances train, Instances test) Generate a bunch of predictions ready for processing, by performing a evaluation on a test set after training on the given training set.
FastVector	EvaluationUtils.getTestPredictions(Classifier classifier, Instances test) Generate a bunch of predictions ready for processing, by performing a evaluation on a test set assuming the classifier is already trained.
Prediction	EvaluationUtils.getPrediction(Classifier classifier, Instance test) Generate a single prediction for a test instance given the pre-trained classifier.

Uses of Classifier in weka.classifiers.functions

Subclasses of Classifier in weka.classifiers.functions

class	LeastMedSq Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions.
class	LinearRegression Class for using linear regression for prediction.
class	Logistic Second implementation for building and using a multinomial logistic regression model with a ridge estimator.
class	MultilayerPerceptron A Classifier that uses backpropagation to classify instances.
class	PaceRegression Class for building pace regression linear models and using them for prediction.
class	RBFNetwork Class that implements a radial basis function network.
class	SimpleLinearRegression Class for learning a simple linear regression model.
class	SimpleLogistic Class for building a logistic regression model using LogitBoost.
class	SMO Implements John C.
class	SMOreg Implements Alex J.Smola and Bernhard Scholkopf sequential minimal optimization algorithm for training a support vector regression using polynomial or RBF kernels.
class	VotedPerceptron Implements the voted perceptron algorithm by Freund and Schapire.
class	Winnow Implements Winnow and Balanced Winnow algorithms by N.

Uses of Classifier in weka.classifiers.lazy

Subclasses of Classifier in weka.classifiers.lazy

class	IB1 IB1-type 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 implement a lazy learning approach to lessening the attribute-independence assumption of naive Bayes.
class	LWL Locally-weighted learning.

Uses of Classifier in weka.classifiers.meta

Subclasses of Classifier in weka.classifiers.meta

class	AdaBoostM1 Class for boosting a classifier using Freund & Schapire's Adaboost M1 method.
class	AdditiveRegression Meta classifier that enhances the performance of a regression base classifier.
class	AttributeSelectedClassifier Class for running an arbitrary classifier on data that has been reduced through attribute selection.
class	Bagging Class for bagging a classifier.
class	ClassificationViaRegression Class for doing classification using regression methods.
class	CostSensitiveClassifier This metaclassifier makes its base classifier cost-sensitive.
class	CVParameterSelection Class for performing parameter selection by cross-validation for any classifier.
class	Decorate DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.
class	END Class for creating a committee of random classifiers.
class	FilteredClassifier Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
class	Grading Implements Grading.
class	HND Class to create levelwise NDs with respect to a given hierarchy of classes.
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 (1999).
class	MultiBoostAB Class for boosting a classifier using the MultiBoosting method.
class	MultiClassClassifier Class for handling multi-class datasets with 2-class distribution 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	ND
class	OrdinalClassClassifier Meta classifier for transforming an ordinal class problem to a series of binary class problems.
class	RacedIncrementalLogitBoost Classifier for incremental learning of large datasets by way of racing logit-boosted committees.
class	RandomCommittee Class for creating a committee of random classifiers.
class	RegressionByDiscretization Class for a regression scheme that employs any distribution classifier on a copy of the data that has the class attribute (equal-width) discretized.
class	Stacking Implements stacking.
class	StackingC Implements StackingC (more efficient version of stacking).
class	ThresholdSelector Class for selecting a threshold on a probability output by a distribution classifier.
class	TreeBasedMultiClassClassifier Class that represents and builds a classifier tree.
class	Vote Class for combining classifiers using unweighted average of probability estimates (classification) or numeric predictions (regression).

Fields in weka.classifiers.meta declared as Classifier

protected Classifier[][]	LogitBoost.m_Classifiers Array for storing the generated base classifiers.
protected Classifier	Stacking.m_MetaClassifier The meta classifier
protected Classifier	AttributeSelectedClassifier.m_Classifier The classifier
protected Classifier	FilteredClassifier.m_Classifier The classifier
protected Classifier	CostSensitiveClassifier.m_Classifier The classifier
protected Classifier	MultiScheme.m_Classifier The classifier that had the best performance on training data.
private Classifier[]	MultiClassClassifier.m_Classifiers The classifiers.
private Classifier	MultiClassClassifier.m_Classifier The class name of the base classifier.
protected Classifier[]	Grading.m_MetaClassifiers The meta classifiers, one for each base classifier.
protected Classifier	ND.m_classifier The base classifier .
private Classifier[]	ClassificationViaRegression.m_Classifiers The classifiers.
protected Classifier[]	StackingC.m_MetaClassifiers The meta classifiers (one for each class, like in ClassificationViaRegression)
protected Classifier	AdditiveRegression.m_Classifier Base classifier.
protected Classifier	RacedIncrementalLogitBoost.m_Classifier The model base classifier to use
private Classifier[]	OrdinalClassClassifier.m_Classifiers The classifiers.
private Classifier	OrdinalClassClassifier.m_Classifier The class name of the base classifier.
protected Classifier	Decorate.m_Classifier The model base classifier to use.
protected Classifier	TreeBasedMultiClassClassifier.m_Classifier The classifier at this node.
protected Classifier	ThresholdSelector.m_Classifier The generated base classifier

Methods in weka.classifiers.meta that return Classifier

Classifier[][]	LogitBoost.classifiers() Returns the array of classifiers that have been built.
Classifier	Stacking.getMetaClassifier() Gets the meta classifier.
Classifier	AttributeSelectedClassifier.getClassifier() Gets the classifier used.
Classifier	FilteredClassifier.getClassifier() Gets the classifier used.
Classifier	CostSensitiveClassifier.getClassifier() Gets the classifier used.
Classifier[]	MultiScheme.getClassifiers() Gets the list of possible classifers to choose from.
Classifier	MultiScheme.getClassifier(int index) Gets a single classifier from the set of available classifiers.
Classifier	MultiClassClassifier.getClassifier() Get the classifier used as the classifier
Classifier	ND.getClassifier() Get the classifier used as the classifier
protected Classifier[]	RacedIncrementalLogitBoost.Committee.boost(Instances data)
Classifier	AdditiveRegression.getClassifier() Gets the classifier used.
Classifier	RacedIncrementalLogitBoost.getClassifier() Get the classifier used as the classifier
Classifier	OrdinalClassClassifier.getClassifier() Get the classifier used as the classifier
Classifier	Decorate.getClassifier() Get the classifier used as the base classifier
Classifier	TreeBasedMultiClassClassifier.getClassifier() Get the classifier used as the classifier * * @return the classifier used as the classifier
Classifier	ThresholdSelector.getClassifier() Get the Classifier used as the classifier.

Methods in weka.classifiers.meta with parameters of type Classifier

void	Stacking.setMetaClassifier(Classifier classifier) Adds meta classifier
void	AttributeSelectedClassifier.setClassifier(Classifier classifier) Sets the classifier
void	FilteredClassifier.setClassifier(Classifier classifier) Sets the classifier
void	CostSensitiveClassifier.setClassifier(Classifier classifier) Sets the distribution classifier
void	MultiScheme.setClassifiers(Classifier[] classifiers) Sets the list of possible classifers to choose from.
void	MultiClassClassifier.setClassifier(Classifier newClassifier) Set the base classifier.
void	ND.setClassifier(Classifier newClassifier) Set the base classifier.
double[]	RacedIncrementalLogitBoost.Committee.updateFS(Instance instance, Classifier[] newModel, double[] Fs)
void	AdditiveRegression.setClassifier(Classifier classifier) Sets the classifier
private Instances	AdditiveRegression.residualReplace(Instances data, Classifier c, boolean useShrinkage) Replace the class values of the instances from the current iteration with residuals ater predicting with the supplied classifier.
void	RacedIncrementalLogitBoost.setClassifier(Classifier newClassifier) Set the classifier for boosting.
void	OrdinalClassClassifier.setClassifier(Classifier newClassifier) Set the base classifier.
void	Decorate.setClassifier(Classifier newClassifier) Set the base classifier for Decorate.
private void	TreeBasedMultiClassClassifier.generateClassifierForNode(Instances data, Range classes, java.util.Random rand, Classifier classifier, boolean random, boolean ordinal) Generates a classifier for the current node and proceeds recursively
void	TreeBasedMultiClassClassifier.setClassifier(Classifier newClassifier) Set the base classifier
void	ThresholdSelector.setClassifier(Classifier newClassifier) Set the Classifier for which threshold is set.

Constructors in weka.classifiers.meta with parameters of type Classifier

FilteredClassifier(Classifier classifier, Filter filter)
Constructor that specifies the subclassifier and filter to use.

AdditiveRegression(Classifier classifier)
Constructor which takes base classifier as argument.

Uses of Classifier in weka.classifiers.misc

Subclasses of Classifier in weka.classifiers.misc

class	FLR Fuzzy Lattice Reasoning Classifier FLR Classifier implementation in WEKA The Fuzzy Lattice Reasoning Classifier uses the notion of Fuzzy Lattices for creating a Reasoning Environment.
class	HyperPipes Class implementing a HyperPipe classifier.
class	VFI Class implementing the voting feature interval classifier.

Uses of Classifier in weka.classifiers.rules

Subclasses of Classifier in weka.classifiers.rules

class	ConjunctiveRule This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels.
class	DecisionTable Class for building and using a simple decision table majority classifier.
class	JRip This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which is proposed by William W.
class	M5Rules Generates a decision list for regression problems using separate-and-conquer.
class	NNge NNge classifier.
class	OneR Class for building and using a 1R classifier.
class	PART Class for generating a PART decision list.
class	Prism Class for building and using a PRISM rule set for classifcation.
class	Ridor The implementation of a RIpple-DOwn Rule learner.
class	ZeroR Class for building and using a 0-R classifier.

Uses of Classifier in weka.classifiers.trees

Subclasses of Classifier in weka.classifiers.trees

class	ADTree Class for generating an alternating decision tree.
class	DecisionStump Class for building and using a decision stump.
class	Id3 Class implementing an Id3 decision tree classifier.
class	J48 Class for generating an unpruned or a pruned C4.5 decision tree.
class	LMT Class for "logistic model tree" classifier.
class	M5P M5P.
class	RandomForest Class for constructing random forests.
class	RandomTree Class for constructing a tree that considers K random features at each node.
class	REPTree Fast decision tree learner.
class	UserClassifier Class for generating an user defined decision tree.

Fields in weka.classifiers.trees declared as Classifier

Classifier

UserClassifier.TreeClass.m_classObject Used instead of the standard leaf if one exists.

Methods in weka.classifiers.trees with parameters of type Classifier

void	UserClassifier.TreeClass.setClassifier(Classifier c) Call this to set an alternate classifier For this node.

Uses of Classifier in weka.classifiers.trees.lmt

Subclasses of Classifier in weka.classifiers.trees.lmt

class	LMTNode Class for logistic model tree structure.
class	LogisticBase Base/helper class for building logistic regression models with the LogitBoost algorithm.

Uses of Classifier in weka.classifiers.trees.m5

Subclasses of Classifier in weka.classifiers.trees.m5

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

Uses of Classifier in weka.experiment

Fields in weka.experiment declared as Classifier

protected Classifier	ClassifierSplitEvaluator.m_Classifier The classifier used for evaluation
protected Classifier	RegressionSplitEvaluator.m_Classifier The classifier used for evaluation

Methods in weka.experiment that return Classifier

Classifier	ClassifierSplitEvaluator.getClassifier() Get the value of Classifier.
Classifier	RegressionSplitEvaluator.getClassifier() Get the value of Classifier.

Methods in weka.experiment with parameters of type Classifier

void	ClassifierSplitEvaluator.setClassifier(Classifier newClassifier) Sets the classifier.
void	RegressionSplitEvaluator.setClassifier(Classifier newClassifier) Sets the classifier.

Uses of Classifier in weka.filters.unsupervised.instance

Fields in weka.filters.unsupervised.instance declared as Classifier

protected Classifier

RemoveMisclassified.m_cleansingClassifier The classifier used to do the cleansing

Methods in weka.filters.unsupervised.instance that return Classifier

Classifier

RemoveMisclassified.getClassifier() Gets the classifier used by the filter.

Methods in weka.filters.unsupervised.instance with parameters of type Classifier

void	RemoveMisclassified.setClassifier(Classifier classifier) Sets the classifier to classify instances with.

Uses of Classifier in weka.gui.beans

Fields in weka.gui.beans declared as Classifier

private Classifier	IncrementalClassifierEvaluator.m_classifier
private Classifier	Classifier.m_Classifier
protected Classifier	BatchClassifierEvent.m_classifier The classifier
protected Classifier	IncrementalClassifierEvent.m_classifier
private Classifier	ClassifierPerformanceEvaluator.m_classifier Holds the classifier to be evaluated

Methods in weka.gui.beans that return Classifier

Classifier	Classifier.getClassifier() Get the classifier currently set for this wrapper
Classifier	BatchClassifierEvent.getClassifier() Get the classifier
Classifier	IncrementalClassifierEvent.getClassifier() Get the classifier

Methods in weka.gui.beans with parameters of type Classifier

void	Classifier.setClassifier(Classifier c) Set the classifier for this wrapper
private Instances	PredictionAppender.makeDataSetProbabilities(Instances format, Classifier classifier, java.lang.String relationNameModifier)
private Instances	PredictionAppender.makeDataSetClass(Instances format, Classifier classifier, java.lang.String relationNameModifier)
void	IncrementalClassifierEvent.setClassifier(Classifier c)

Constructors in weka.gui.beans with parameters of type Classifier

BatchClassifierEvent(java.lang.Object source, Classifier scheme, Instances tstI, int setNum, int maxSetNum)
Creates a new BatchClassifierEvent instance.

IncrementalClassifierEvent(java.lang.Object source, Classifier scheme, Instance currentI, int status)
Creates a new IncrementalClassifierEvent instance.

Uses of Classifier in weka.gui.boundaryvisualizer

Fields in weka.gui.boundaryvisualizer declared as Classifier

private Classifier	RemoteBoundaryVisualizerSubTask.m_classifier
protected Classifier	BoundaryPanel.m_classifier
private Classifier	BoundaryVisualizer.m_classifier

Methods in weka.gui.boundaryvisualizer with parameters of type Classifier

void	RemoteBoundaryVisualizerSubTask.setClassifier(Classifier dc) Set the classifier to use
void	BoundaryPanel.setClassifier(Classifier classifier) Set the classifier to use.
void	BoundaryVisualizer.setClassifier(Classifier newClassifier) Set a classifier to use

Uses of Classifier in weka.gui.experiment

Methods in weka.gui.experiment with parameters of type Classifier

private void

AlgorithmListPanel.addNewAlgorithm(Classifier newScheme) Add a new algorithm to the list.

Uses of Classifier in weka.gui.explorer

Methods in weka.gui.explorer with parameters of type Classifier

private void	ClassifierPanel.processClassifierPrediction(Instance toPredict, Classifier classifier, Evaluation eval, FastVector predictions, Instances plotInstances, FastVector plotShape, FastVector plotSize) Process a classifier's prediction for an instance and update a set of plotting instances and additional plotting info. plotInfo for nominal class datasets holds shape types (actual data points have automatic shape type assignment; classifier error data points have box shape type).
protected java.lang.String	ClassifierPanel.predictionText(Classifier classifier, Instance inst, int instNum)
protected void	ClassifierPanel.saveClassifier(java.lang.String name, Classifier classifier, Instances trainHeader) Saves the currently selected classifier
protected void	ClassifierPanel.reevaluateModel(java.lang.String name, Classifier classifier, Instances trainHeader) Re-evaluates the named classifier with the current test set.