# Data Driving Multiple Classifier System

## Documentation for package ‘D2MCS’ version 1.0.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

Accuracy | Computes the Accuracy measure.
BinaryPlot | Plotting feature clusters following bi-class problem.
ChiSquareHeuristic | Feature-clustering based on ChiSquare method.
ClassificationOutput | D2MCS Classification Output.
ClassMajorityVoting | Implementation of Majority Voting voting.
ClassWeightedVoting | Implementation Weighted Voting scheme.
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.
FisherTestHeuristic | Feature-clustering based on Fisher's Exact Test.
FN | Computes the False Negative errors.
FP | Computes the False Positive value.
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.
NoProbability | Compute performance across resamples.
NPV | Computes the Negative Predictive Value.
OddsRatioHeuristic | Feature-clustering based on Odds Ratio measure.
PearsonHeuristic | Feature-clustering based on Pearson Correlation Test.
PPV | Computes the Positive Predictive Value.
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.