A high‑dimensionality‑trait‑driven learning paradigm for high dimensional credit classification

查看全文 作　　者：Lean [1,2]Yu;Lihang [1]Yu;Kaitao [3]Yu

机构地区：[1]School of Economics and Management,Beijing University of Chemical Technology,15 Beisanhuan East Road,Chaoyang District,Beijing 100029,China;[2]School of Economics and Management,University of Chinese Academy of Sciences,80 Zhongguancun East Road,Haidian District,Beijing 100190,China;[3]Canada International School of Beijing,Liangmaqiao Road,Chaoyang District,Beijing 100125,China

出　　处：《Financial Innovation》索引2021年第7卷第1期,共20页

基　　金：This work is partially supported by grants from the Key Program of National Natural Science Foundation of China(NSFC Nos.71631005 and 71731009);the Major Program of the National Social Science Foundation of China(No.19ZDA103). 摘　　要：To solve the high-dimensionality issue and improve its accuracy in credit risk assessment,a high-dimensionality-trait-driven learning paradigm is proposed for feature extraction and classifier selection.The proposed paradigm consists of three main stages:categorization of high dimensional data,high-dimensionality-trait-driven feature extraction,and high-dimensionality-trait-driven classifier selection.In the first stage,according to the definition of high-dimensionality and the relationship between sample size and feature dimensions,the high-dimensionality traits of credit dataset are further categorized into two types:100 关键词：High dimensionality Trait-driven learning paradigm Feature extraction Classifier selection Credit risk classification

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A high‑dimensionality‑trait‑driven learning paradigm for high dimensional credit classification

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