Package weka.classifiers.meta.multisearch

Interface Summary

Interface	Description
MultiSearchCapable	Interface for multi-search capable classifiers..
TraceableOptimizer

Class Summary

Class	Description
AbstractEvaluationFactory<M extends AbstractEvaluationMetrics,W extends AbstractEvaluationWrapper,T extends AbstractEvaluationTask,E>	Factory for creating evaluation-related objects.
AbstractEvaluationMetrics	Ancestor for evaluation metrics (to be used in the GUI).
AbstractEvaluationTask	Helper class for evaluating a setup.
AbstractEvaluationWrapper<T,M extends AbstractEvaluationMetrics>	Wrapper for evaluation classes.
AbstractMultiThreadedSearch	Ancestor for multi-threaded searches.
AbstractSearch	Ancestor for search algorithms.
AbstractSearch.SearchResult	Container class for the search results.
DefaultEvaluationFactory	Default factory.
DefaultEvaluationMetrics	Default metrics.
DefaultEvaluationTask	Default Evaluation task.
DefaultEvaluationWrapper	Wrapper for the Evaluation class.
DefaultSearch	Performs a search of an arbitrary number of parameters of a classifier and chooses the best setup found for the actual training. The properties being explored are totally up to the user. 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: - 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: 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).
Performance	A helper class for storing the performance of values in the parameter space.
PerformanceCache	Represents a simple cache for performance objects.
PerformanceComparator	A concrete Comparator for the Performance class.
RandomSearch	Performs a search of an arbitrary number of parameters of a classifier and chooses the best pair found for the actual filtering and training. Valid options are: