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 |
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