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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 |
| 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 |
| Reference manual: | npcs.pdf |
| Vignettes: |
npcs-demo |
| 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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