Class Summary |
ActiveDecorate |
Active-DECORATE is a version of DECORATE that allows for selective
sampling of training examples. |
AdaBoostM1 |
Class for boosting a classifier using Freund & Schapire's Adaboost
M1 method. |
AdditiveRegression |
Meta classifier that enhances the performance of a regression base
classifier. |
AttributeSelectedClassifier |
Class for running an arbitrary classifier on data that has been reduced
through attribute selection. |
Bagging |
Class for bagging a classifier. |
ClassificationViaRegression |
Class for doing classification using regression methods. |
CostSensitiveClassifier |
This metaclassifier makes its base classifier cost-sensitive. |
Crate |
CRATE (Committee Regressor using Artificial Training Examples) is a
meta-learner for building diverse ensembles of regressors by
adding specially constructed artificial training
examples. |
CVParameterSelection |
Class for performing parameter selection by cross-validation for any
classifier. |
DEC |
Class for creating Diverse Ensembles of a Classifier
Valid options are: |
Decorate |
DECORATE is a meta-learner for building diverse ensembles of
classifiers by using specially constructed artificial training
examples. |
DistributionMetaClassifier |
Class for wrapping a Classifier to make it return a distribution. |
Fable |
FABLE is a version of DECORATE that allows for active feature
acquisition. |
FilteredClassifier |
Class for running an arbitrary classifier on data that has been passed
through an arbitrary filter. |
LogitBoost |
Class for boosting any classifier that can handle weighted instances. |
MetaCost |
This metaclassifier makes its base classifier cost-sensitive using the
method specified in |
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 |
Class for handling multi-class datasets with 2-class distribution
classifiers. |
MultiScheme |
Class for selecting a classifier from among several using cross
validation on the training data. |
OrdinalClassClassifier |
Meta classifier for transforming an ordinal class problem to a series
of binary class problems. |
QBag |
This class implements Query-by-Bagging based on Abe and Mamitsuka (ICML 98). |
QBoost |
This class implements Query-by-Boosting based on Abe and Mamitsuka (ICML 98). |
RacedIncrementalLogitBoost |
Classifier for incremental learning of large datasets by way of racing logit-boosted committees. |
RegressionByDiscretization |
Class for a regression scheme that employs any distribution
classifier on a copy of the data that has the class attribute
discretized. |
SemiSupDecorate |
Class for creating Semi-Supervised Diverse Ensembles of a Classifier
Valid options are: |
Stacking |
Implements stacking. |
TestEnsembleClassifier |
This class is for testing Ensemble evaluation |
ThresholdSelector |
Class for selecting a threshold on a probability output by a
distribution classifier. |