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An optimized support vector machine intelligent technique using optimized feature selection methods: evidence from Chinese credit approval data

Abedin Abedin, Chi Guotai, Fahmida-E- Moula, Tong Zhang, M. Kabir Hassan

Journal of Risk Model Validation, Volume: 13, Issue: 2, Pages: 1 - 46

Swansea University Author: Abedin Abedin

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DOI (Published version): 10.21314/jrmv.2019.206

Abstract

This paper focuses on feature selection methods for support vector machine (SVM) classifiers, checking their optimality by comparing them with some statistical and baseline methods. To achieve the above objective, we exploit twelve feature selection methods from the family of filters and embedded ap...

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Published in: Journal of Risk Model Validation
ISSN: 1753-9579 1753-9587
Published: Infopro Digital Services Limited 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa64258
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Abstract: This paper focuses on feature selection methods for support vector machine (SVM) classifiers, checking their optimality by comparing them with some statistical and baseline methods. To achieve the above objective, we exploit twelve feature selection methods from the family of filters and embedded approaches by splitting a Chinese database. Our findings suggest that the average result from sample division cases will achieve a more robust prediction ability than that from “no sample division” cases. Moreover, ridge regression (SVM9) in training and “average results from sample division” data sets, along with DTQUEST (SVM7) in “no sample division” example sets, give outstanding performance with respect to all performance criteria. With these contributions, therefore, our paper complements previous evidence and modernizes the methods of feature selection to render SVM classifiers favorable for credit approval data modeling. This study has practical implications for financial institutions, managers, employees, investors and government officials looking to sort out forthcoming lending transactions to attain a target risk/return trade-off. © Infopro Digital Limited. All rights reserved.
Keywords: Support vector machine, SVM classifiers, credit approval data, China
College: Faculty of Humanities and Social Sciences
Issue: 2
Start Page: 1
End Page: 46