| weka.classifiers.meta |
|
| Java Source File Name | Type | Comment |
| AdaBoostM1.java | Class |
Class for boosting a nominal class classifier using the Adaboost M1 method. |
| AdditiveRegression.java | Class |
Meta classifier that enhances the performance of a regression base classifier. |
| AttributeSelectedClassifier.java | Class |
Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
Valid options are:
-E <attribute evaluator specification>
Full class name of attribute evaluator, followed
by its options.
eg: "weka.attributeSelection.CfsSubsetEval -L"
(default weka.attributeSelection.CfsSubsetEval)
-S <search method specification>
Full class name of search method, followed
by its options.
eg: "weka.attributeSelection.BestFirst -D 1"
(default weka.attributeSelection.BestFirst)
-D
If set, classifier is run in debug mode and
may output additional info to the console
-W
Full name of base classifier.
(default: weka.classifiers.trees.J48)
Options specific to classifier weka.classifiers.trees.J48:
-U
Use unpruned tree.
-C <pruning confidence>
Set confidence threshold for pruning.
(default 0.25)
-M <minimum number of instances>
Set minimum number of instances per leaf.
(default 2)
-R
Use reduced error pruning.
-N <number of folds>
Set number of folds for reduced error
pruning. |
| Bagging.java | Class |
Class for bagging a classifier to reduce variance. |
| ClassificationViaRegression.java | Class |
Class for doing classification using regression methods. |
| CostSensitiveClassifier.java | Class |
A metaclassifier that makes its base classifier cost-sensitive. |
| CVParameterSelection.java | Class |
Class for performing parameter selection by cross-validation for any classifier.
For more information, see:
R. |
| Dagging.java | Class |
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.java | Class |
DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples. |
| END.java | Class |
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.java | Class |
Combines several classifiers using the ensemble selection method. |
| FilteredClassifier.java | Class |
Class for running an arbitrary classifier on data that has been passed through an arbitrary filter. |
| Grading.java | Class |
Implements Grading. |
| GridSearch.java | Class |
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.java | Class |
Class for performing additive logistic regression. |
| MetaCost.java | Class |
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.java | Class |
Class for boosting a classifier using the MultiBoosting method.
MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. |
| MultiClassClassifier.java | Class |
A metaclassifier for handling multi-class datasets with 2-class classifiers. |
| MultiScheme.java | Class |
Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data. |
| OrdinalClassClassifier.java | Class |
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.java | Class |
Classifier for incremental learning of large datasets by way of racing logit-boosted committees. |
| RandomCommittee.java | Class |
Class for building an ensemble of randomizable base classifiers. |
| RandomSubSpace.java | Class |
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.java | Class |
A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized. |
| Stacking.java | Class |
Combines several classifiers using the stacking method. |
| StackingC.java | Class |
Implements StackingC (more efficient version of stacking).
For more information, see
A.K. |
| ThresholdSelector.java | Class |
A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier. |
| Vote.java | Class |
Class for combining classifiers. |