Package weka.classifiers.meta

Class Summary

AbstainAttributePercentile	Only predict if attribute value within percentile range.
AbstainLeastMedianSq	Finds the base classifier with the best least median squared error.
ClassificationViaRegressionD	Class for doing classification using regression methods.
Corr	Assume NO MISSING VALUES, all attributes must be NUMERIC (or 0/1 maybe ...).
FilteredClassifierExt	Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
LeastMedianSq	Finds the base classifier with the best least median squared error.
LogTargetRegressor	Takes logs of all numeric attributes in the data.
MultiSearch	Performs a search of an arbitrary number of parameters of a classifier and chooses the best pair found for the actual filtering and training. The default MultiSearch is using the following FilteredClassifier setup: - classifier: LinearRegression, searching for the "Ridge" - filter: PLSFilter, searching for the "# of Components" The properties being explored are totally up to the user, it can be a mix of classifier and filter properties, or only classifier ones or only filter ones. Since the the MultiSearch classifier itself is used as the base object for the setups being generated, one has to prefix the properties with 'classifier.' (referring to MultiSearch's 'classifier' property). E.g., if you have a FilteredClassifier selected as base classifier, sporting a PLSFilter and you want to explore the number of PLS components, then your property will be made up of the following components: - classifier: referring to MultiSearch's classifier property i.e., the FilteredClassifier. - filter: referring to the FilteredClassifier's property (= PLSFilter) - numComponents: the actual property of the PLSFilter that we want to modify And assembled, the property looks like this: classifier.filter.numComponents The initial space is worked on with 2-fold CV to determine the values of the parameters for the selected type of evaluation (e.g., accuracy).
MultiSearch.EvaluationTask	Helper class for evaluating a setup.
PartitionedStacking	Builds the base-classifiers on subsets of the data defined by ranges that correspond to the base-classifiers.
PeakTransformed	Uses the maximum peak in the instances.
RangeCheck	Keeps track of the ranges in case of numeric attributes.
SubsetEnsemble	Generates an ensemble using the following approach: - for each attribute apart from class attribute do: * create new dataset with only this feature and the class attribute * remove all instances that contain a missing value * if no instances left in subset, don't build a classifier for this feature * if at least 1 instance is left in subset, build base classifier with it If no classifier gets built at all, use ZeroR as backup model, built on the full dataset. In addition to the default feature for a subset, a number of random features can be added to the subset before the classifier is trained. At prediction time, the Vote meta-classifier (using the pre-built classifiers) is used to determing the class probabilities or regression value.
SumTransformed	Finds the base classifier with the best least median squared error.
WeightedInstancesHandlerWrapper	A meta-classifier that implements the weka.core.WeightedInstancesHandler interface in order to enable all classifiers to be used in other meta-classifiers that require the base classifier to implem ent the WeightedInstancesHandler interface.