Package weka.classifiers.meta

Class Summary
ActiveDecorate Active-DECORATE is a version of DECORATE that allows for selective sampling of training examples.
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.
Crate CRATE (Committee Regressor using Artificial Training Examples) is a meta-learner for building diverse ensembles of regressors by adding specially constructed artificial training examples.
CVParameterSelection Class for performing parameter selection by cross-validation for any classifier.
DEC Class for creating Diverse Ensembles of a Classifier Valid options are:
Decorate DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.
DistributionMetaClassifier Class for wrapping a Classifier to make it return a distribution.
Fable FABLE is a version of DECORATE that allows for active feature acquisition.
FilteredClassifier Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
LogitBoost Class for boosting any classifier that can handle weighted instances.
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.
OrdinalClassClassifier Meta classifier for transforming an ordinal class problem to a series of binary class problems.
QBag This class implements Query-by-Bagging based on Abe and Mamitsuka (ICML 98).
QBoost This class implements Query-by-Boosting based on Abe and Mamitsuka (ICML 98).
RacedIncrementalLogitBoost Classifier for incremental learning of large datasets by way of racing logit-boosted committees.
RegressionByDiscretization Class for a regression scheme that employs any distribution classifier on a copy of the data that has the class attribute discretized.
SemiSupDecorate Class for creating Semi-Supervised Diverse Ensembles of a Classifier Valid options are:
Stacking Implements stacking.
TestEnsembleClassifier This class is for testing Ensemble evaluation
ThresholdSelector Class for selecting a threshold on a probability output by a distribution classifier.