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D2MCS (version 1.0.1)

Data Driving Multiple Classifier System

Description

Provides a novel framework to able to automatically develop and deploy an accurate Multiple Classifier System based on the feature-clustering distribution achieved from an input dataset. 'D2MCS' was developed focused on four main aspects: (i) the ability to determine an effective method to evaluate the independence of features, (ii) the identification of the optimal number of feature clusters, (iii) the training and tuning of ML models and (iv) the execution of voting schemes to combine the outputs of each classifier comprising the Multiple Classifier System.

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install.packages('D2MCS')

Monthly Downloads

206

Version

1.0.1

License

GPL-3

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Maintainer

Miguel Ferreiro-D<c3><ad>az

Last Published

August 23rd, 2022

Functions in D2MCS (1.0.1)

Accuracy

Computes the Accuracy measure.
CombinedVoting

Implementation of Combined Voting.
ChiSquareHeuristic

Feature-clustering based on ChiSquare method.
ClassMajorityVoting

Implementation of Majority Voting voting.
BinaryPlot

Plotting feature clusters following bi-class problem.
ClusterPredictions

Manages the predictions achieved on a cluster.
CombinedMetrics

Abstract class to compute the class prediction based on combination between metrics.
ConfMatrix

Confusion matrix wrapper.
DefaultModelFit

Default model fitting implementation.
FP

Computes the False Positive value.
DependencyBasedStrategy

Clustering strategy based on dependency between features.
Dataset

Simple Dataset handler.
DependencyBasedStrategyConfiguration

Custom Strategy Configuration handler for the DependencyBasedStrategy strategy.
FIterator

Iterator over a file.
FinalPred

Stores the prediction for a specific voting scheme.
DatasetLoader

Dataset creation.
ExecutedModels

Handles training of M.L. models
MultinformationHeuristic

Feature-clustering based on Mutual Information Computation theory.
FN

Computes the False Negative errors.
GenericClusteringStrategy

Abstract Feature Clustering Strategy class.
Methodology

Abstract class to compute the probability prediction based on combination between metrics.
HDDataset

High Dimensional Dataset handler.
MinimizeFN

Combined metric strategy to minimize FN errors.
GenericHeuristic

Abstract Feature Clustering heuristic object.
KendallHeuristic

Feature-clustering based on Kendall Correlation Test.
ClassWeightedVoting

Implementation Weighted Voting scheme.
ClassificationOutput

D2MCS Classification Output.
MCC

Computes the Matthews correlation coefficient.
HDSubset

High Dimensional Subset handler.
D2MCS

Data Driven Multiple Classifier System.
NPV

Computes the Negative Predictive Value.
GenericModelFit

Abstract class for defining model fitting method.
GenericPlot

Pseudo-abstract class for creating feature clustering plots.
Subset

Classification set.
DIterator

Iterator over a Subset object
Precision

Computes the Precision Value.
SummaryFunction

Abstract class to computing performance across resamples.
Prediction

Manages the prediction computed for a specific model.
NoProbability

Compute performance across resamples.
SingleVoting

Manages the execution of Simple Votings.
SpearmanHeuristic

Feature-clustering based on Spearman Correlation Test.
FisherTestHeuristic

Feature-clustering based on Fisher's Exact Test.
MinimizeFP

Combined metric strategy to minimize FP errors.
OddsRatioHeuristic

Feature-clustering based on Odds Ratio measure.
TrainFunction

Control parameters for train stage.
ProbAverageWeightedVoting

Implementation of Probabilistic Average Weighted voting.
ProbBasedMethodology

Methodology to obtain the combination of the probability of different metrics.
TypeBasedStrategy

Feature clustering strategy.
Model

Stores a previously trained M.L. model.
TrainOutput

Stores the results achieved during training.
MCCHeuristic

Feature-clustering based on Matthews Correlation Coefficient score.
GainRatioHeuristic

Feature-clustering based on GainRatio methodology.
PredictionOutput

Encapsulates the achieved predictions.
InformationGainHeuristic

Feature-clustering based on InformationGain methodology.
UseProbability

Compute performance across resamples.
ProbAverageVoting

Implementation of Probabilistic Average voting.
Kappa

Computes the Kappa Cohen value.
Recall

Computes the Recall Value.
SimpleStrategy

Simple feature clustering strategy.
MeasureFunction

Archetype to define customized measures.
Sensitivity

Computes the Sensitivity Value.
PPV

Computes the Positive Predictive Value.
SimpleVoting

Abtract class to define simple voting schemes.
PearsonHeuristic

Feature-clustering based on Pearson Correlation Test.
Specificity

Computes the Specificity Value.
TN

Computes the True Negative value.
StrategyConfiguration

Default Strategy Configuration handler.
Trainset

Trainning set.
VotingStrategy

Voting Strategy template.
TwoClass

Control parameters for train stage (Bi-class problem).
TP

Computes the True Positive Value.