Package weka.classifiers.meta

Class Summary

AdaBoostM1	Class for boosting a classifier using Freund & Schapire's Adaboost M1 method.
AdditiveRegression	Meta classifier that enhances the performance of a regression base classifier.
AttributeSelectedClassifier	Class for running an arbitrary classifier on data that has been reduced through attribute selection.
Bagging	Class for bagging a classifier.
ClassificationViaRegression	Class for doing classification using regression methods.
CostSensitiveClassifier	This metaclassifier makes its base classifier cost-sensitive.
CVParameterSelection	Class for performing parameter selection by cross-validation for any classifier.
Decorate	DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.
FilteredClassifier	Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
Grading	Implements Grading.
LogitBoost	Class for performing additive logistic regression..
MetaCost	This metaclassifier makes its base classifier cost-sensitive using the method specified in
MultiBoostAB	Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees.
MultiClassClassifier	Class for handling multi-class datasets with 2-class distribution classifiers.
MultiScheme	Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
OrdinalClassClassifier	Meta classifier for transforming an ordinal class problem to a series of binary class problems.
RacedIncrementalLogitBoost	Classifier for incremental learning of large datasets by way of racing logit-boosted committees.
RandomCommittee	Class for creating a committee of random classifiers.
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.
Stacking	Implements stacking.
StackingC	Implements StackingC (more efficient version of stacking).
ThresholdSelector	Class for selecting a threshold on a probability output by a distribution classifier.
Vote	Class for combining classifiers using unweighted average of probability estimates (classification) or numeric predictions (regression).