Package: D2MCS 1.0.1
D2MCS: Data Driving Multiple Classifier System
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.
Authors:
D2MCS_1.0.1.tar.gz
D2MCS_1.0.1.zip(r-4.5)D2MCS_1.0.1.zip(r-4.4)D2MCS_1.0.1.zip(r-4.3)
D2MCS_1.0.1.tgz(r-4.4-any)D2MCS_1.0.1.tgz(r-4.3-any)
D2MCS_1.0.1.tar.gz(r-4.5-noble)D2MCS_1.0.1.tar.gz(r-4.4-noble)
D2MCS_1.0.1.tgz(r-4.4-emscripten)D2MCS_1.0.1.tgz(r-4.3-emscripten)
D2MCS.pdf |D2MCS.html✨
D2MCS/json (API)
NEWS
# Install 'D2MCS' in R: |
install.packages('D2MCS', repos = c('https://drordas.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/drordas/d2mcs/issues
Last updated 2 years agofrom:257090d1ba. Checks:OK: 5 NOTE: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 11 2024 |
R-4.5-win | NOTE | Oct 11 2024 |
R-4.5-linux | NOTE | Oct 11 2024 |
R-4.4-win | OK | Oct 11 2024 |
R-4.4-mac | OK | Oct 11 2024 |
R-4.3-win | OK | Oct 11 2024 |
R-4.3-mac | OK | Oct 11 2024 |
Exports:AccuracyBinaryPlotChiSquareHeuristicClassificationOutputClassMajorityVotingClassWeightedVotingClusterPredictionsCombinedMetricsCombinedVotingConfMatrixD2MCSDatasetDatasetLoaderDefaultModelFitDependencyBasedStrategyDependencyBasedStrategyConfigurationDIteratorExecutedModelsFisherTestHeuristicFIteratorFNFPGainRatioHeuristicGenericClusteringStrategyGenericHeuristicGenericModelFitGenericPlotHDDatasetHDSubsetInformationGainHeuristicKappaKendallHeuristicMCCMCCHeuristicMeasureFunctionMethodologyMinimizeFNMinimizeFPModelMultinformationHeuristicNoProbabilityNPVOddsRatioHeuristicPearsonHeuristicPPVPrecisionPredictionPredictionOutputProbAverageVotingProbAverageWeightedVotingProbBasedMethodologyRecallSensitivitySimpleStrategySimpleVotingSingleVotingSpearmanHeuristicSpecificityStrategyConfigurationSubsetSummaryFunctionTNTPTrainFunctionTrainOutputTrainsetTwoClassTypeBasedStrategyUseProbabilityVotingStrategy
Dependencies:askpassbase64encbitbit64brewbriobslibcachemcallrcaretclassclassIntclicliprclockcodetoolscolorspacecommonmarkcpp11crayoncredentialscurldata.tabledescdevtoolsdiagramdiffobjdigestdownlitdplyre1071ellipsisentropyevaluatefansifarverfastmapfontawesomeforcatsforeachfsFSelectorfuturefuture.applygenericsgertggplot2ggrepelghgitcredsglobalsgluegowergridExtragtablehardhathavenhighrhmshtmltoolshtmlwidgetshttpuvhttr2infotheoiniipredisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglabelledlaterlatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmccrmemoisemgcvmimeminiUImltoolsModelMetricsmunsellnlmennetnumDerivopensslparallellypillarpkgbuildpkgconfigpkgdownpkgloadplyrpraiseprettyunitspROCprocessxprodlimprofvisprogressprogressrpromisesproxypspurrrquestionrR.cacheR.methodsS3R.ooR.utilsR6raggrandomForestrappdirsrcmdcheckRColorBrewerRcppreadrrecipesrematch2remotesreshape2rJavarlangrmarkdownroxygen2rpartrprojrootrstudioapirversionsRWekaRWekajarssassscalessessioninfoshapeshinysourcetoolsSQUAREMstringistringrstylersurvivalsyssystemfontstestthattextshapingtibbletictoctidyrtidyselecttimechangetimeDatetinytextzdburlcheckerusethisutf8varhandlevctrsviridisLitevroomwaldowhiskerwithrxfunxml2xopenxtableyamlzip