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), which will be posted on arXiv soon.


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0.1.0 by Ye Tian, a month ago

Browse source code at

Authors: Ye Tian [aut, cre] , Yang Feng [aut]

Documentation:   PDF Manual  

GPL-2 license

Imports dfoptim, nnet, randomForest, e1071, magrittr, MASS, smotefamily, rpart, foreach, naivebayes, caret, formatR

Suggests knitr, rmarkdown

See at CRAN