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.
Version: 1.0.1 Depends: R (≥ 4.2) Imports: caret, devtools, dplyr, FSelector, ggplot2, ggrepel, gridExtra, infotheo, mccr, mltools, ModelMetrics, questionr, recipes, R6, tictoc, varhandle Suggests: grDevices, knitr, rmarkdown, testthat (≥ 3.0.2) Published: 2022-08-23 DOI: 10.32614/CRAN.package.D2MCS Author: David Ruano-Ordás [aut, ctb], Miguel Ferreiro-DÃaz [aut, cre], José Ramón Méndez [aut, ctb], University of Vigo [cph] Maintainer: Miguel Ferreiro-DÃaz <miguel.ferreiro.diaz at gmail.com> BugReports: https://github.com/drordas/D2MCS/issues License: GPL-3 URL: https://github.com/drordas/D2MCS NeedsCompilation: no Citation: D2MCS citation info Materials: NEWS CRAN checks: D2MCS resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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