CRAN/E | npcs

npcs

Neyman-Pearson Classification via Cost-Sensitive Learning

Installation

About

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).

Key Metrics

Version 0.1.1
R ≥ 3.5.0
Published 2023-04-27 336 days ago
Needs compilation? no
License GPL-2
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Maintainer

Maintainer

Ching-Tsung Tsai

tctsung@nyu.edu

Authors

Ye Tian

aut

Ching-Tsung Tsai

aut / cre

Yang Feng

aut

Material

Reference manual
Package source

Vignettes

npcs-demo

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

npcs archive

Depends

R ≥ 3.5.0

Imports

dfoptim
magrittr
smotefamily
foreach
caret
formatR
dplyr
forcats
ggplot2
tidyr
nnet

Suggests

knitr
rmarkdown
gbm