weka.classifiers.meta

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## 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.
EnsembleSelection | Combines several classifiers using the ensemble selection method.
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.
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.

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