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D2MCS
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]
[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 21 Nov. 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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