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

Documentation:

Reference manual:	npcs.pdf
Vignettes:	npcs-demo

Downloads:

Package source:	npcs_0.1.1.tar.gz
Windows binaries:	r-devel: npcs_0.1.1.zip, r-release: npcs_0.1.1.zip, r-oldrel: npcs_0.1.1.zip
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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