A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies. For m…

Class for running an arbitrary classifier on data that has been passed through an arbitrary filter. Like the classifier, the stru…

Implements Grading. The base classifiers are "graded". For more information, see A.K. Seewald, J. Fuernkranz: An Evaluation of Gr…

Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) a…

Class for performing additive logistic regression. This class performs classification using a regression scheme as the base learn…

Class for boosting a nominal class classifier using the Adaboost M1 method. Only nominal class problems can be tackled. Often dra…

Meta classifier that enhances the performance of a regression base classifier. Each iteration fits a model to the residuals left …

Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier. (based on WEKA…

Class for bagging a classifier to reduce variance. Can do classification and regression depending on the base learner. For more i…

A simple meta-classifier that uses a clusterer for classification. For cluster algorithms that use a fixed number of clusterers, …

Class for doing classification using regression methods. Class is binarized and one regression model is built for each class valu…

This metaclassifier makes its base classifier cost-sensitive using the method specified in Pedro Domingos: MetaCost: A general me…

Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost t…

A metaclassifier for handling multi-class datasets with 2-class classifiers. This classifier is also capable of applying error co…

Class for selecting a classifier from among several using cross validation on the training data or the performance on the trainin…

A meta classifier for handling multi-class datasets with 2-class classifiers by building a random class-balanced tree structure. …

A metaclassifier that makes its base classifier cost-sensitive. Two methods can be used to introduce cost-sensitivity: reweightin…

Class for performing parameter selection by cross-validation for any classifier. For more information, see: R. Kohavi (1995). Wra…

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…

DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examp…