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

计算机与自动化 | Updated：2022-12-09

- Credit scoring by feature-weighted support vector machines
- Credit scoring by feature-weighted support vector machines
- 信息与电子工程前沿（英文） 2013年14卷第3期页码：197-204
- 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：
  
  Project supported by the National Basic Research Program (973) of China (No.2011CB706506), the National Natural Science Foundation of China (No.50905159), the Natural Science Foundation of Jiangsu Province (No.BK2010261), and the Fundamental Research Funds for the Central Universities (No.2011XZZX005), China
- DOI：10.1631/jzus.C1200205
  中图分类号： TP391.4
- 收稿：2012-06-28，
  
  修回：2013-;2-25，
  
  纸质出版：2013-03
- Accepted：
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Credit scoring by feature-weighted support vector machines[J]. 信息与电子工程前沿（英文）, 2013,14(3):197-204.

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.
Credit scoring by feature-weighted support vector machines[J]. 信息与电子工程前沿（英文）, 2013,14(3):197-204. DOI： 10.1631/jzus.C1200205.

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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