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npcs (version 0.1.1)

Neyman-Pearson Classification via Cost-Sensitive Learning

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).

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Version

Install

install.packages('npcs')

Monthly Downloads

178

Version

0.1.1

License

GPL-2

Maintainer

Ching-Tsung Tsai

Last Published

April 27th, 2023

Functions in npcs (0.1.1)

generate_data

Generate the data.
gamma_smote

Gamma-synthetic minority over-sampling technique (gamma-SMOTE).
npcs

Fit a multi-class Neyman-Pearson classifier with error controls via cost-sensitive learning.
cv.npcs

Compare the performance of the NPMC-CX, NPMC-ER, and vanilla models through cross-validation or bootstrapping methods
print.cv.npcs

Print the cv.npcs object.
predict.npcs

Predict new labels from new data based on the fitted NPMC classifier.
error_rate

Calculate the error rates for each class.