Credit Risk Evaluation Using ES Based SVM-MK

DOI

10.2991/icmia-16.2016.122How to use a DOI?

Keywords

Credit risk evaluation, SVM-MK, ES SVM-MK,SVM.

Abstract

Under the background of big data recent studies have revealed that emerging modern machine learning techniques are advantageous to statistical models for credit risk evaluation, such as SVM. In this study, we discuss the applications of the evolution strategies based support vector machine with mixture of kernel(ES based SVM-MK) to design a credit evaluation system, which can discriminate good creditors from bad ones. Differing from the standard SVM, the SVM-MK uses the 1-norm based object function and adopts the convex combinations of single feature basic kernels. Only a linear programming problem needs to be resolved and it greatly reduces the computational costs. A real life credit dataset from a US commercial bank is used to demonstrate the good performance of the ES SVM- MK.

Copyright

© 2016, 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  - Liwei Wei
AU  - Ying Zhang
AU  - Mochen Liu
AU  - Qiang Xiao
PY  - 2016/11
DA  - 2016/11
TI  - Credit Risk Evaluation Using ES Based SVM-MK
BT  - Proceedings of the 2016 5th International Conference on Measurement, Instrumentation and Automation (ICMIA 2016)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmia-16.2016.122
DO  - 10.2991/icmia-16.2016.122
ID  - Wei2016/11
ER  -