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: |
1.0.0 |
| Depends: |
R (≥ 4.0) |
| 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: |
2021-05-07 |
| Author: |
David Ruano-Ordás [aut, ctb],
Miguel Ferreiro-Díaz [aut, cre],
José Ramón Méndez [aut, ctb],
University of Vigo [cph] |
| Maintainer: |
Miguel Ferreiro-Díaz <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 results |