We connect the multi-class Neyman-Pearson classification (NP) problem to the cost-sensitive learning (CS) problem, and propose two algorithms (NPMC-CX and NPMC-ER) to solve the multi-class NP problem through cost-sensitive learning tools. Under certain conditions, the two algorithms are shown to satisfy multi-class NP properties. More details are available in the paper "Neyman-Pearson Multi-class Classification via Cost-sensitive Learning" (Ye Tian and Yang Feng, 2021).
Version: 0.1.1 Depends: R (≥ 3.5.0) Imports: dfoptim, magrittr, smotefamily, foreach, caret, formatR, dplyr, forcats, ggplot2, tidyr, nnet Suggests: knitr, rmarkdown, gbm Published: 2023-04-27 DOI: 10.32614/CRAN.package.npcs Author: Ye Tian [aut], Ching-Tsung Tsai [aut, cre], Yang Feng [aut] Maintainer: Ching-Tsung Tsai <tctsung at nyu.edu> License: GPL-2 NeedsCompilation: no CRAN checks: npcs results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=npcs to link to this page.
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