Package weka.classifiers.meta

package weka.classifiers.meta
  • Classes
    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.