All Classes

Class Summary

Class	Description
CoevolutionaryMaxOnes	Dummy test for coevolutionary problems.
CoevolutionaryStatistics	Coevolutionary statistics.
CVIFSNormalizer	This class allows to normalize CVI values for Feature Selection problems.
DatasetSplitter	Split a dataset into a training and test datasets given a test proportion, that is, the proportion of data reserved to the test dataset.
DaviesBouldin	This class implements the CVI proposed by Davies and Bouldin in [1].
DunnIndex	This class implements the CVI proposed by Dunn in [1].
ELBG	Implements the ELBG algorithm [1]
FarthestCentroids	This index estimates the separation of clusters as the addition of distances from each centroid to its farthest centroid.
FSAbstractDistance	Abstract super class for all distances used to solve Feature Selection problems
FSCrossValidator	Test the results of a Feature Selector via cross-validation
FSDefaults	Default class for all the Feature Selection problems.
FSErrorRateValidator	Obtain the training and test error rates of the results of a Feature Selector
FSEuclideanDistance	This class implements the Euclidean distance.
FSKappaValidator	Obtain the training and test Kappa indices of the results of a Feature Selector
FSManhattanDistance	The Manhattan distance is the sum of the (absolute) differences of their coordinates.
FSParetoFrontAnalyzer	Obtain the relevance of each feature in the whole Pareto front.
FSRanker	Translate a relevances file into a ranks file [1]
FSRelevancesAnalyzer	Gather the relevances from several experiments
FSSensitivityValidator	Obtain the training and test sensitivities of the results of a Feature Selector
FSSpecificityValidator	Obtain the training and test specificities of the results of a Feature Selector
FSStabilitySpearmanScorer	Computes the Spearman score achieved by a set of experiments
FSSubsetCrossoverPipeline	FSSubsetCrossoverPipeline is a BreedingPipeline which implements a simple default crossover for a FSSubsetIndividual.
FSSubsetDefaults	Default class for all the Feature Selection problems solved using individuals coded as subsets of feature indices.
FSSubsetIndividual	FSSubsetIndividual is an individual whose genome is a subset of selected features for a feature selection problem.
FSSubsetMutationPipeline	FSSubsetMutationPipeline is a BreedingPipeline which implements a simple default Mutation for FSSubsetIndividual.
FSSubsetProblem	Base abstract class for Feature Selection problems solved using individuals coded as subsets of feature indices.
FSSubsetSpecies	FSSubsetSpecies is a species which can create FSSubsetIndividual.
FSSubsetSupervisedCoevolutionaryProblem	Co-evolutive Lexicographic Multi-objective wrapper for supervised (labeled) datasets solved using a subset of feature indices as individuals representation.
FSSubsetSupervisedProblem	Multi-objective wrapper for supervised (labeled) datasets solved using a subset of feature indices as individuals representation.
FSSubsetUnsupervisedProblem	Multi-objective feature selection for unsupervised datasets solved using a subset of feature indices as individuals representation.
KMedians	Implement the K-medians algorithm.
KolmogorovSmirnovNormalityTest	Applies a Kolmogorov-Smirnov normality test to a set of values and prints the p-value
LBG	Implements the LBG algorithm [1].
LexicographicFitness	Lexicographic Fitness is a subclass of Fitness which implements a fitness estimation composed of several objectives that have to be optimized.
MaxClusterDiameter	This is a cluster cohesion index based on the cohesion criterion used by the Dunn index [1].
MinFarthestCentroid	This index estimates the separation of clusters as the minimum of the max distance from a centroid to all the remaining centroids.
MoreDatasetTools	Add several tools to those provided by Java-ML.
MultiObjectiveStatistics	MultiObjective Statistics.
NaiveBayes	Implementation of the Naive Bayes classification algorithm.
OverallDeviation	The Overall Deviation Criterion is cluster cohesion index proposed in [1].
PerformanceIndexes	This class implements some performance metrics based on the confusion matrix of a classifier
RandomFeatureAdder	Append some random features to a training dataset, and also to a test dataset if provided
TestCVI	Test a separation and a compactness CVI
TestDirectLDA	Test the direct LDA method
TestLibSVM	Test SVM as a wrapper classifier
TestNaiveBayes	Test Naive Bayes as a wrapper classifier
TestNormalization	Test of the normalization function of the MoreDatasetTools class
TestSplit	Test of the split function of the MoreDatasetTools class
Zdt4	Implementation of the zdt4 benchmark [1].