Neyman-Pearson Multi-class Classification via Cost-sensitive Learning

IF 3 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the American Statistical Association Pub Date : 2024-09-20 DOI:10.1080/01621459.2024.2402567

Ye Tian, Yang Feng

引用次数: 0

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...

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通过成本敏感型学习进行奈曼-皮尔逊多类分类

现有的大多数分类方法都以最小化整体误分类错误率为目标。然而，在贷款违约预测等应用中，不同类型的错误会产生不同的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of the American Statistical Association 数学-统计学与概率论

CiteScore

7.50

自引率

8.10%

发文量

168

审稿时长

12 months

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.