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.
This work was supported by the foundation of Beijing Key Lab of Intelligent Information Technology.
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Wei, H., Li, J. (2011). Credit Scoring Based on Eigencredits and SVDD. In: Zeng, D. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23220-6_5
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DOI: https://doi.org/10.1007/978-3-642-23220-6_5
Publisher Name: Springer, Berlin, Heidelberg
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