<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">ASDS</journal-id><journal-title-group><journal-title>Applied Statistics and Data Science</journal-title></journal-title-group><issn>3066-8433</issn><eissn>3066-8441</eissn><publisher><publisher-name>Art and Design</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/ASDS.2025090010</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>非均等误判代价下信用特征选择的HHG-CB-CSR协同模型</title><url>https://artdesignp.com/journal/ASDS/1/9/10.61369/ASDS.2025090010</url><author>王静赛,熊志斌</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>1</volume><issue>9</issue><history><date date-type="pub"><published-time>2025-11-20</published-time></date></history><abstract>特征选择是信用评估的关键环节。针对信用数据的类别不平衡、非均等误判代价及类别型特征多等问题，本文首先基于代价敏感学习提出代价敏感查全率（CSR） 指标；进而融合Heller-Heller-Gorfine（HHG）检验与CatBoost，构建HHG-CB-CSR 信用特征选择方法&amp;mdash;&amp;mdash; 以HHG 检验指导序列后向搜索，CatBoost 为学习器，CSR 为特征子集评价与停止准则。该方法可解决高基数类别型特征数值化难题，精准度量特征相关性并赋予选择过程代价敏感性。4个信用数据集的实证表明，HHG-CB-CSR 在传统指标与CSR 指标上均表现优异，稳健性与实际应用性突出。</abstract><keywords>信用评估,特征选择,代价敏感查全率,HHG 检验,CatBoost</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] Altman E I, Marco G, Varetto F. 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