Class | Description |
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AdaBoostM1 |
Class for boosting a nominal class classifier using the Adaboost M1 method.
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AdditiveRegression |
Meta classifier that enhances the performance of a regression base classifier.
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AttributeSelectedClassifier |
Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
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Bagging |
Class for bagging a classifier to reduce variance.
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ClassificationViaClustering |
A simple meta-classifier that uses a clusterer for classification.
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ClassificationViaRegression |
Class for doing classification using regression methods.
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CostSensitiveClassifier |
A metaclassifier that makes its base classifier cost-sensitive.
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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.
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Decorate |
DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.
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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.
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Grading |
Implements Grading.
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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.
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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.
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MultiScheme |
Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
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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.
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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.
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RegressionByDiscretization |
A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.
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RotationForest |
Class for construction a Rotation Forest.
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Stacking |
Combines several classifiers using the stacking method.
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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.
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Vote |
Class for combining classifiers.
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