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npcs  

Neyman-Pearson Classification via Cost-Sensitive Learning
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Install from CRAN:
install.packages("npcs")

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library("remotes")
install_github("cran/npcs")

Install by package version:
library("remotes")
install_version("npcs", "0.1.1")



Attach the package and use:
library("npcs")
Maintained by
Ching-Tsung Tsai
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-10-19
Latest Update: 2023-04-27
Description:
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).
How to cite:
Ching-Tsung Tsai (2021). npcs: Neyman-Pearson Classification via Cost-Sensitive Learning. R package version 0.1.1, https://cran.r-project.org/web/packages/npcs. Accessed 03 Dec. 2024.
Previous versions and publish date:
0.1.0 (2021-10-19 09:20)
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Complete documentation for npcs
Functions, R codes and Examples using the npcs R package
Some associated functions: cv.npcs . error_rate . gamma_smote . generate_data . npcs . predict.npcs . print.cv.npcs . 
Some associated R codes: cv.npcs.R . error_rate.R . funcs.R . gamma_smote.R . generate_data.R . npcs.R . predict.npcs.R . print.cv.npcs.R .  Full npcs package functions and examples
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