Variable selection in AUC-optimizing classification

IF 1.6 3区 数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Hyungwoo Kim , Seung Jun Shin
{"title":"Variable selection in AUC-optimizing classification","authors":"Hyungwoo Kim ,&nbsp;Seung Jun Shin","doi":"10.1016/j.csda.2025.108256","DOIUrl":null,"url":null,"abstract":"<div><div>Optimizing the receiver operating characteristic (ROC) curve is a popular way to evaluate a binary classifier under imbalanced scenarios frequently encountered in practice. A practical approach to constructing a linear binary classifier is presented by simultaneously optimizing the area under the ROC curve (AUC) and selecting informative variables in high dimensions. In particular, the smoothly clipped absolute deviation (SCAD) penalty is employed, and its oracle property is established, which enables the development of a consistent BIC-type information criterion that greatly facilitates the tuning procedure. Both simulated and real data analyses demonstrate the promising performance of the proposed method in terms of AUC optimization and variable selection.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"213 ","pages":"Article 108256"},"PeriodicalIF":1.6000,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Statistics & Data Analysis","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S016794732500132X","RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

Optimizing the receiver operating characteristic (ROC) curve is a popular way to evaluate a binary classifier under imbalanced scenarios frequently encountered in practice. A practical approach to constructing a linear binary classifier is presented by simultaneously optimizing the area under the ROC curve (AUC) and selecting informative variables in high dimensions. In particular, the smoothly clipped absolute deviation (SCAD) penalty is employed, and its oracle property is established, which enables the development of a consistent BIC-type information criterion that greatly facilitates the tuning procedure. Both simulated and real data analyses demonstrate the promising performance of the proposed method in terms of AUC optimization and variable selection.
auc优化分类中的变量选择
优化接收者工作特征(ROC)曲线是在实践中经常遇到的不平衡场景下评估二值分类器的常用方法。提出了一种构建线性二元分类器的实用方法,即同时优化ROC曲线下面积(AUC)和选择高维信息变量。特别地,采用了平滑裁剪绝对偏差(SCAD)惩罚,并建立了其oracle属性,从而能够开发一致的bic类型信息标准,大大简化了调优过程。仿真和实际数据分析均证明了该方法在AUC优化和变量选择方面具有良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis 数学-计算机:跨学科应用
CiteScore
3.70
自引率
5.60%
发文量
167
审稿时长
60 days
期刊介绍: Computational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of four refereed sections which are divided into the following subject areas: I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics,computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms. Software and algorithms can be submitted with manuscripts and will be stored together with the online article. II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures. [...] III) Special Applications - [...] IV) Annals of Statistical Data Science [...]
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信