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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