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).
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.1 |
rolling linux/jammy R-4.5 | npcs_0.1.1.tar.gz |
357.9 KiB |
0.1.1 |
rolling linux/noble R-4.5 | npcs_0.1.1.tar.gz |
357.8 KiB |
0.1.1 |
rolling source/ R- | npcs_0.1.1.tar.gz |
572.2 KiB |
0.1.1 |
latest linux/jammy R-4.5 | npcs_0.1.1.tar.gz |
357.9 KiB |
0.1.1 |
latest linux/noble R-4.5 | npcs_0.1.1.tar.gz |
357.8 KiB |
0.1.1 |
latest source/ R- | npcs_0.1.1.tar.gz |
572.2 KiB |
0.1.1 |
2026-04-26 source/ R- | npcs_0.1.1.tar.gz |
572.2 KiB |
0.1.1 |
2026-04-23 source/ R- | npcs_0.1.1.tar.gz |
572.2 KiB |
0.1.1 |
2026-04-09 windows/windows R-4.5 | npcs_0.1.1.zip |
360.4 KiB |
0.1.1 |
2025-04-20 source/ R- | npcs_0.1.1.tar.gz |
572.2 KiB |