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D2MCS  

Data Driving Multiple Classifier System
View on CRAN: Click here


Download and install D2MCS package within the R console
Install from CRAN:
install.packages("D2MCS")

Install from Github:
library("remotes")
install_github("cran/D2MCS")

Install by package version:
library("remotes")
install_version("D2MCS", "1.0.1")



Attach the package and use:
library("D2MCS")
Maintained by
Miguel Ferreiro-Díaz
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-05-07
Latest Update: 2022-08-23
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.
How to cite:
Miguel Ferreiro-Díaz (2021). D2MCS: Data Driving Multiple Classifier System. R package version 1.0.1, https://cran.r-project.org/web/packages/D2MCS. Accessed 22 Dec. 2024.
Previous versions and publish date:
1.0.0 (2021-05-07 11:30)
Other packages that cited D2MCS R package
View D2MCS citation profile
Other R packages that D2MCS depends, imports, suggests or enhances
Complete documentation for D2MCS
Functions, R codes and Examples using the D2MCS R package
Some associated functions: Accuracy . BinaryPlot . ChiSquareHeuristic . ClassMajorityVoting . ClassWeightedVoting . ClassificationOutput . ClusterPredictions . CombinedMetrics . CombinedVoting . ConfMatrix . D2MCS . DIterator . Dataset . DatasetLoader . DefaultModelFit . DependencyBasedStrategy . DependencyBasedStrategyConfiguration . ExecutedModels . FIterator . FN . FP . FinalPred . FisherTestHeuristic . GainRatioHeuristic . GenericClusteringStrategy . GenericHeuristic . GenericModelFit . GenericPlot . HDDataset . HDSubset . InformationGainHeuristic . Kappa . KendallHeuristic . MCC . MCCHeuristic . MeasureFunction . Methodology . MinimizeFN . MinimizeFP . Model . MultinformationHeuristic . NPV . NoProbability . OddsRatioHeuristic . PPV . PearsonHeuristic . Precision . Prediction . PredictionOutput . ProbAverageVoting . ProbAverageWeightedVoting . ProbBasedMethodology . Recall . Sensitivity . SimpleStrategy . SimpleVoting . SingleVoting . SpearmanHeuristic . Specificity . StrategyConfiguration . Subset . SummaryFunction . TN . TP . TrainFunction . TrainOutput . Trainset . TwoClass . TypeBasedStrategy . UseProbability . VotingStrategy . 
Some associated R codes: D2MCS.R . clustering.heuristics.ChiSquareHeuristic.R . clustering.heuristics.FisherTestHeuristic.R . clustering.heuristics.GainRatioHeuristic.R . clustering.heuristics.GenericHeuristic.R . clustering.heuristics.InformationGainHeuristic.R . clustering.heuristics.KendallHeuristic.R . clustering.heuristics.MCCHeuristic.R . clustering.heuristics.MultinformationHeuristic.R . clustering.heuristics.OddsRatioHeuristic.R . clustering.heuristics.PearsonHeuristic.R . clustering.heuristics.SpearmanHeuristic.R . clustering.plot.BinaryPlot.R . clustering.plot.GenericPlot.R . clustering.strategies.DependencyBasedStrategy.R . clustering.strategies.DependencyBasedStrategyConfiguration.R . clustering.strategies.GenericClusteringStrategy.R . clustering.strategies.SimpleStrategy.R . clustering.strategies.StrategyConfiguration.R . clustering.strategies.TypeBasedStrategy.R . data.DIterator.R . data.Dataset.R . data.DatasetLoader.R . data.FIterator.R . data.HDDataset.R . data.HDSubset.R . data.Subset.R . data.Trainset.R . measures.Accuracy.R . measures.ConfMatrix.R . measures.FN.R . measures.FP.R . measures.Kappa.R . measures.MCC.R . measures.MeasureFunction.R . measures.NPV.R . measures.PPV.R . measures.Precision.R . measures.Recall.R . measures.Sensitivity.R . measures.Specificity.R . measures.TN.R . measures.TP.R . models.ExecutedModels.R . models.Model.R . models.summaryFunction.NoProbability.R . models.summaryFunction.SummaryFunction.R . models.summaryFunction.UseProbability.R . models.trainFunctions.TrainFunction.R . models.trainFunctions.TwoClass.R . models.utility.DefaultModelFit.R . models.utility.GenericModelFit.R . prediction.ClassMajorityVoting.R . prediction.ClassWeightedVoting.R . prediction.ClassificationOutput.R . prediction.ClusterPredictions.R . prediction.CombinedMetrics.R . prediction.CombinedVoting.R . prediction.FinalPred.R . prediction.Methodology.R . prediction.MinimizeFN.R . prediction.MinimizeFP.R . prediction.Prediction.R . prediction.PredictionOutput.R . prediction.ProbAverageVoting.R . prediction.ProbAverageWeightedVoting.R . prediction.ProbBasedMethodology.R . prediction.SimpleVoting.R . prediction.SingleVoting.R . prediction.TrainOutput.R . prediction.VotingStrategy.R .  Full D2MCS package functions and examples
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