Traditional noise filtering methods aim at removing noisy samples from a classification dataset. This package adapts classic and recent filtering techniques for use in regression problems, and it also incorporates methods specifically designed for regression data. In order to do this, it uses approaches proposed in the specialized literature, such as Martin et al. (2021) [<doi:10.1109/ACCESS.2021.3123151>] and Arnaiz-Gonzalez et al. (2016) [<doi:10.1016/j.eswa.2015.12.046>]. Thus, the goal of the implemented noise filters is to eliminate samples with noise in regression datasets.
Version: 1.1.3 Depends: R (≥ 3.2.0) Imports: e1071, FNN, gbm, modelr, nnet, randomForest, rpart, arules, infotheo, entropy, ggplot2, class, kknn Suggests: testthat (≥ 3.0.0), knitr, rmarkdown Published: 2025-05-23 DOI: 10.32614/CRAN.package.rgnoisefilt Author: Juan Martin [aut, cre], José A. Sáez [aut], Emilio Corchado [aut], Pablo Morales [ctb] (Author of the NoiseFiltersR package), Julian Luengo [ctb] (Author of the NoiseFiltersR package), Luis P.F. Garcia [ctb] (Author of the NoiseFiltersR package), Ana C. Lorena [ctb] (Author of the NoiseFiltersR package), Andre C.P.L.F. de Carvalho [ctb] (Author of the NoiseFiltersR package), Francisco Herrera [ctb] (Author of the NoiseFiltersR package) Maintainer: Juan Martin <juanmartin at usal.es> License: GPL (≥ 3) URL: https://github.com/juanmartinsantos/rgnoisefilt NeedsCompilation: no Materials: NEWS CRAN checks: rgnoisefilt results Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=rgnoisefilt to link to this page.
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