{"title":"通过成本敏感型学习进行奈曼-皮尔逊多类分类","authors":"Ye Tian, Yang Feng","doi":"10.1080/01621459.2024.2402567","DOIUrl":null,"url":null,"abstract":"Most existing classification methods aim to minimize the overall misclassification error rate. However, in applications such as loan default prediction, different types of errors can have varying c...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"106 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Neyman-Pearson Multi-class Classification via Cost-sensitive Learning\",\"authors\":\"Ye Tian, Yang Feng\",\"doi\":\"10.1080/01621459.2024.2402567\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Most existing classification methods aim to minimize the overall misclassification error rate. However, in applications such as loan default prediction, different types of errors can have varying c...\",\"PeriodicalId\":17227,\"journal\":{\"name\":\"Journal of the American Statistical Association\",\"volume\":\"106 1\",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the American Statistical Association\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1080/01621459.2024.2402567\",\"RegionNum\":1,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American Statistical Association","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1080/01621459.2024.2402567","RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
Neyman-Pearson Multi-class Classification via Cost-sensitive Learning
Most existing classification methods aim to minimize the overall misclassification error rate. However, in applications such as loan default prediction, different types of errors can have varying c...
期刊介绍：
Established in 1888 and published quarterly in March, June, September, and December, the Journal of the American Statistical Association ( JASA ) has long been considered the premier journal of statistical science. Articles focus on statistical applications, theory, and methods in economic, social, physical, engineering, and health sciences. Important books contributing to statistical advancement are reviewed in JASA .
JASA is indexed in Current Index to Statistics and MathSci Online and reviewed in Mathematical Reviews. JASA is abstracted by Access Company and is indexed and abstracted in the SRM Database of Social Research Methodology.
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