Accuracy                Computes the Accuracy measure.
BinaryPlot              Plotting feature clusters following bi-class
                        problem.
ChiSquareHeuristic      Feature-clustering based on ChiSquare method.
ClassMajorityVoting     Implementation of Majority Voting voting.
ClassWeightedVoting     Implementation Weighted Voting scheme.
ClassificationOutput    D2MCS Classification Output.
ClusterPredictions      Manages the predictions achieved on a cluster.
CombinedMetrics         Abstract class to compute the class prediction
                        based on combination between metrics.
CombinedVoting          Implementation of Combined Voting.
ConfMatrix              Confusion matrix wrapper.
D2MCS                   Data Driven Multiple Classifier System.
Dataset                 Simple Dataset handler.
DatasetLoader           Dataset creation.
DefaultModelFit         Default model fitting implementation.
DependencyBasedStrategy
                        Clustering strategy based on dependency between
                        features.
DependencyBasedStrategyConfiguration
                        Custom Strategy Configuration handler for the
                        DependencyBasedStrategy strategy.
FN                      Computes the False Negative errors.
FP                      Computes the False Positive value.
FisherTestHeuristic     Feature-clustering based on Fisher's Exact
                        Test.
GainRatioHeuristic      Feature-clustering based on GainRatio
                        methodology.
GenericClusteringStrategy
                        Abstract Feature Clustering Strategy class.
GenericHeuristic        Abstract Feature Clustering heuristic object.
GenericModelFit         Abstract class for defining model fitting
                        method.
GenericPlot             Pseudo-abstract class for creating feature
                        clustering plots.
HDDataset               High Dimensional Dataset handler.
HDSubset                High Dimensional Subset handler.
InformationGainHeuristic
                        Feature-clustering based on InformationGain
                        methodology.
Kappa                   Computes the Kappa Cohen value.
KendallHeuristic        Feature-clustering based on Kendall Correlation
                        Test.
MCC                     Computes the Matthews correlation coefficient.
MCCHeuristic            Feature-clustering based on Matthews
                        Correlation Coefficient score.
MeasureFunction         Archetype to define customized measures.
Methodology             Abstract class to compute the probability
                        prediction based on combination between
                        metrics.
MinimizeFN              Combined metric strategy to minimize FN errors.
MinimizeFP              Combined metric strategy to minimize FP errors.
MultinformationHeuristic
                        Feature-clustering based on Mutual Information
                        Computation theory.
NPV                     Computes the Negative Predictive Value.
NoProbability           Compute performance across resamples.
OddsRatioHeuristic      Feature-clustering based on Odds Ratio measure.
PPV                     Computes the Positive Predictive Value.
PearsonHeuristic        Feature-clustering based on Pearson Correlation
                        Test.
Precision               Computes the Precision Value.
PredictionOutput        Encapsulates the achieved predictions.
ProbAverageVoting       Implementation of Probabilistic Average voting.
ProbAverageWeightedVoting
                        Implementation of Probabilistic Average
                        Weighted voting.
ProbBasedMethodology    Methodology to obtain the combination of the
                        probability of different metrics.
Recall                  Computes the Recall Value.
Sensitivity             Computes the Sensitivity Value.
SimpleStrategy          Simple feature clustering strategy.
SimpleVoting            Abtract class to define simple voting schemes.
SingleVoting            Manages the execution of Simple Votings.
SpearmanHeuristic       Feature-clustering based on Spearman
                        Correlation Test.
Specificity             Computes the Specificity Value.
StrategyConfiguration   Default Strategy Configuration handler.
Subset                  Classification set.
SummaryFunction         Abstract class to computing performance across
                        resamples.
TN                      Computes the True Negative value.
TP                      Computes the True Positive Value.
TrainFunction           Control parameters for train stage.
TrainOutput             Stores the results achieved during training.
Trainset                Trainning set.
TwoClass                Control parameters for train stage (Bi-class
                        problem).
TypeBasedStrategy       Feature clustering strategy.
UseProbability          Compute performance across resamples.
VotingStrategy          Voting Strategy template.
