Package: rgnoisefilt 1.1.2

rgnoisefilt: Elimination of Noisy Samples in Regression Datasets using Noise Filters

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.

Authors:Juan Martin [aut, cre], José A. Sáez [aut], Emilio Corchado [aut], Pablo Morales [ctb], Julian Luengo [ctb], Luis P.F. Garcia [ctb], Ana C. Lorena [ctb], Andre C.P.L.F. de Carvalho [ctb], Francisco Herrera [ctb]

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Bug tracker:https://github.com/juanmartinsantos/rgnoisefilt/issues

On CRAN:

22 exports 2 stars 1.18 score 53 dependencies 3 scripts 182 downloads

Last updated 12 months agofrom:3c460cb292. Checks:OK: 3 NOTE: 4. Indexed: yes.

Exports:discCNNdiscENNdiscNCLdiscTLregAENNregBBNRregCNNregCVCFregDFregEFregENNregFMFregGEregHRRFregIPFregIRFregRNDregRNNrfCDFrfDROP2rfDROP3rfMIF

Dependencies:arulesbackportsbroomclassclicolorspacecpp11dplyre1071entropyfansifarverFNNgbmgenericsggplot2gluegtableigraphinfotheoisobandkknnlabelinglatticelifecyclemagrittrMASSMatrixmgcvmodelrmunsellnlmennetpillarpkgconfigproxypurrrR6randomForestRColorBrewerrlangrpartscalesstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr