Credit scoring by feature-weighted support vector machines

Jian SHI; Shu-you ZHANG; Le-miao QIU

doi:10.1631/jzus.C1200205

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Credit scoring by feature-weighted support vector machines

Computer & Automation | Updated：2022-12-09

- Credit scoring by feature-weighted support vector machines
- Credit scoring by feature-weighted support vector machines
- Frontiers of Information Technology & Electronic Engineering Vol. 14, Issue 3, Pages: 197-204(2013)
- Affiliations：
  
  1.The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
  2.School of Electrical and Automatic Engineering, Changshu Institute of Technology, Changshu 215500, China
- Author bio：
  
  [ "Jian SHI, Email:yievans@zju.edu.cn" ]
  [ "Le-miao QIU, Email:qiulm@zju.edu.cn" ]
- Funds：
- DOI：10.1631/jzus.C1200205
  CLC： TP391.4
- Received：28 June 2012，
  
  Revised：2013-;2-25，
  
  Published：2013-03
- Accepted：
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Jian SHI, Shu-you ZHANG, Le-miao QIU. Credit scoring by feature-weighted support vector machines[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(3): 197-204.
DOI：

Jian SHI, Shu-you ZHANG, Le-miao QIU. Credit scoring by feature-weighted support vector machines[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(3): 197-204. DOI： 10.1631/jzus.C1200205.

摘要

Abstract

Recent finance and debt crises have made credit risk management one of the most important issues in financial research. Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics. In this paper

a novel feature-weighted support vector machine (SVM) credit scoring model is presented for credit risk assessment

in which an

-score is adopted for feature importance ranking. Considering the mutual interaction among modeling features

random forest is further introduced for relative feature importance measurement. These two feature-weighted versions of SVM are tested against the traditional SVM on two real-world datasets and the research results reveal the validity of the proposed method.

关键词

Keywords

references

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