Credit scoring based on eigencredits and SVDD

Haizhou Wei*, Jianwu Li

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Credit risk evaluation is an active research topic in financial risk management, and credit scoring is an important analytical technique in credit risk evaluation. In this paper, a new two-stage method is introduced to perform credit scoring. Eigencredits are firstly constructed based on creditworthy examples through principal component analysis to extract the principal features of creditworthy data. Then, support vector domain description (SVDD) is further used to describe creditworthy examples. Preliminary experiments based on two real data sets from UCI repository show the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationApplied Informatics and Communication - International Conference, ICAIC 2011, Proceedings
Pages32-40
Number of pages9
EditionPART 2
DOIs
Publication statusPublished - 2011
Event2011 International Conference on Applied Informatics and Communication, ICAIC 2011 - Xi'an, China
Duration: 20 Aug 201121 Aug 2011

Publication series

NameCommunications in Computer and Information Science
NumberPART 2
Volume225 CCIS
ISSN (Print)1865-0929

Conference

Conference2011 International Conference on Applied Informatics and Communication, ICAIC 2011
Country/TerritoryChina
CityXi'an
Period20/08/1121/08/11

Keywords

  • Credit scoring
  • Eigencredits
  • Support vector domain description (SVDD)

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