Package weka.classifiers.meta

Class Summary

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.
ClassificationViaClustering	A simple meta-classifier that uses a clusterer for classification.
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.
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.
Decorate	DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.
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.
FilteredClassifier	Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
Grading	Implements Grading.
GridSearch	Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting. The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy).
LogitBoost	Class for performing additive logistic regression.
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.
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	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.
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.
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.
RandomCommittee	Class for building an ensemble of randomizable base classifiers.
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.
RotationForest	Class for construction a Rotation Forest.
Stacking	Combines several classifiers using the stacking method.
StackingC	Implements StackingC (more efficient version of stacking). For more information, see A.K.
ThresholdSelector	A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier.
Vote	Class for combining classifiers.