npcs: Neyman-Pearson Classification via Cost-Sensitive Learning

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>
License: GPL-2
NeedsCompilation: no
CRAN checks: npcs results


Reference manual: npcs.pdf
Vignettes: npcs-demo


Package source: npcs_0.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): npcs_0.1.1.tgz, r-oldrel (arm64): npcs_0.1.1.tgz, r-release (x86_64): npcs_0.1.1.tgz, r-oldrel (x86_64): npcs_0.1.1.tgz
Old sources: npcs archive


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