NDLI: Combining multiple classifiers based on Dempster-Shafer theory for personal credit scoring

Content Provider	IEEE Xplore Digital Library
Author	Jiang Ming-hui Hu Jian-hua
Copyright Year	2014
Description	Author affiliation: Sch. of Manage., Harbin Inst. of Technol., Harbin, China (Jiang Ming-hui; Hu Jian-hua)
Abstract	With the rapid development of China's consumer credit market, personal credit scoring has attracted more and more attention. And a variety of statistical and artificial intelligence methods have been used in personal credit scoring. This paper presents a multiple classifier fusion method, namely using relevant theory and methods to combine single classifiers. This is for making full use of the complementary information between classifiers, and ultimately achieves the purpose of better classification results. We present an investigation into the fusion of two different classification methods for personal credit scoring, using Dempster-Shafer's rule of fusion. These methods include traditional linear Logistic Regression and newly developing nonlinear BP neural network. Our experiment's results show that the performance of the fusion of these two different classifiers on real consumer credit customer data is good. Its classification accuracy is better than that of the individual method and its type II error rate is effectively reduced, taking the advantage of the fusion of two classifiers. And it exerts a very important significance on the control of the risk of commercial banks' business.
Starting Page	167
Ending Page	172
File Size	185041
Page Count	6
File Format	PDF
ISBN	9781479953752
ISSN	21551855
e-ISBN	9781479953769
DOI	10.1109/ICMSE.2014.6930225
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-17
Publisher Place	Finland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	fusion personal credit scoring Accuracy Biological system modeling Neural networks Education Robustness DS evidence theory Modeling Logistics
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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