Resampling approach for one-Class classification; Resampling approach for one-Class classification

被引：0
作者：
Lee H.-H. [1 ]
Park S. [2 ]
Im J. [1 ]
机构：
[1] Department of Statistics and Data Science, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul
[2] Department of Information and Statistics, Kangwon National University, 1, Kangwondaehak-gil, Chuncheon-si, Gangwon-do
来源：
Pattern Recognition | 2023年 / 143卷
基金：
新加坡国家研究基金会;
关键词：
Calibration; Data-driven approach; One-class classification; Oversampling;
D O I：
10.1016/j.patcog.2023.109731
中图分类号：
学科分类号：
摘要：
The performance of a classification model depends significantly on the degree to which the support of each data class overlaps. Successfully distinguishing between classes is difficult if the support is similar. In the one-class classification (OCC) problem, wherein the data comprise only a single class, the classifier performance is significantly degraded if the population support of each class is similar. In this study, we propose a resampling algorithm that enhances classifier performance by utilizing the macro information that is most easily obtainable in these two problem situations. The algorithm aims to improve classifier performance by reprocessing the given data into data with mitigated class imbalance through raking and sampling techniques. This performance improvement is demonstrated by comparing representative classifiers used in the existing OCC problem with traditional binary classifier models, which are unavailable on a single-class dataset. © 2023 The Author(s)
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