Empirical Study on Indicators Selection Model Based on significant Discrimination and R Clustering Analysis

DOI

10.2991/isci-15.2015.302How to use a DOI?

Keywords

credit evaluation; indicators selection; logistic regression; R clustering analysis.

Abstract

Small enterprises play the important role in pushing China’s economic progress, but keeping on facing the difficulty in financing the loans. Establishing a reasonable credit evaluation indicators system is one of the keys to implement accurate credit evaluate to small enterprises. Regardless of the evaluation method being used, with unsuitable indicators system, it is impossible to obtain reasonable credit evaluation results. By the application of logistic regression significant discrimination and R clustering analysis, a small enterprises credit evaluation indicators system is established. The credit evaluation system established in this paper is capable of significantly discriminating default samples from non-default ones and can effectively avoids duplicate information. The result of empirical study shows that the credit evaluation indicators system established in this paper is able to reflect 83.47% of original information with 22.22% of original indicators.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lingling Gong
AU  - Guotai Chi
AU  - Baofeng Shi
AU  - Wei Yao
PY  - 2015/01
DA  - 2015/01
TI  - Empirical Study on Indicators Selection Model Based on significant Discrimination and R Clustering Analysis
BT  - Proceedings of the 2015 International Symposium on Computers & Informatics
PB  - Atlantis Press
SP  - 2316
EP  - 2329
SN  - 2352-538X
UR  - https://doi.org/10.2991/isci-15.2015.302
DO  - 10.2991/isci-15.2015.302
ID  - Gong2015/01
ER  -