Package weka.classifiers.meta

Class Summary

Class	Description
AdaBoostM1	Class for boosting a nominal class classifier using the Adaboost M1 method.
AdditiveRegression	Meta classifier that enhances the performance of a regression base classifier.
AttributeSelectedClassifier	Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
Bagging	Class for bagging a classifier to reduce variance.
ClassificationViaRegression	Class for doing classification using regression methods.
CostSensitiveClassifier	A metaclassifier that makes its base classifier cost-sensitive.
CVParameterSelection	Class for performing parameter selection by cross-validation for any classifier. For more information, see: R.
FilteredClassifier	Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
IterativeClassifierOptimizer	Chooses the best number of iterations for an IterativeClassifier such as LogitBoost using cross-validation or a percentage split evaluation.
LogitBoost	Class for performing additive logistic regression.
MultiClassClassifier	A metaclassifier for handling multi-class datasets with 2-class classifiers.
MultiClassClassifierUpdateable	A metaclassifier for handling multi-class datasets with 2-class classifiers.
MultiScheme	Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
RandomCommittee	Class for building an ensemble of randomizable base classifiers.
RandomizableFilteredClassifier	Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
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.
RegressionByDiscretization	A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.
Stacking	Combines several classifiers using the stacking method.
Vote	Class for combining classifiers.
WeightedInstancesHandlerWrapper	Generic wrapper around any classifier to enable weighted instances support. Uses resampling with weights if the base classifier is not implementing the weka.core.WeightedInstancesHandler interface and there are instance weights other 1.0 present.