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. |