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

Mohammad Abedin Orcid Logo, 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: Mohammad Abedin Orcid Logo

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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
first_indexed 2023-09-19T09:34:04Z
last_indexed 2024-11-25T14:13:44Z
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spelling 2023-09-20T10:31:36.3601704 v2 64258 2023-08-31 An optimized support vector machine intelligent technique using optimized feature selection methods: evidence from Chinese credit approval data 4ed8c020eae0c9bec4f5d9495d86d415 0000-0002-4688-0619 Mohammad Abedin Mohammad Abedin true false 2023-08-31 CBAE 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. Journal Article Journal of Risk Model Validation 13 2 1 46 Infopro Digital Services Limited 1753-9579 1753-9587 Support vector machine, SVM classifiers, credit approval data, China 30 6 2019 2019-06-30 10.21314/jrmv.2019.206 http://dx.doi.org/10.21314/jrmv.2019.206 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University 2023-09-20T10:31:36.3601704 2023-08-31T17:57:25.0706322 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Mohammad Abedin 0000-0002-4688-0619 1 Chi Guotai 2 Fahmida-E- Moula 3 Tong Zhang 4 M. Kabir Hassan 5
title An optimized support vector machine intelligent technique using optimized feature selection methods: evidence from Chinese credit approval data
spellingShingle An optimized support vector machine intelligent technique using optimized feature selection methods: evidence from Chinese credit approval data
Mohammad Abedin
title_short An optimized support vector machine intelligent technique using optimized feature selection methods: evidence from Chinese credit approval data
title_full An optimized support vector machine intelligent technique using optimized feature selection methods: evidence from Chinese credit approval data
title_fullStr An optimized support vector machine intelligent technique using optimized feature selection methods: evidence from Chinese credit approval data
title_full_unstemmed An optimized support vector machine intelligent technique using optimized feature selection methods: evidence from Chinese credit approval data
title_sort An optimized support vector machine intelligent technique using optimized feature selection methods: evidence from Chinese credit approval data
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
author Mohammad Abedin
author2 Mohammad Abedin
Chi Guotai
Fahmida-E- Moula
Tong Zhang
M. Kabir Hassan
format Journal article
container_title Journal of Risk Model Validation
container_volume 13
container_issue 2
container_start_page 1
publishDate 2019
institution Swansea University
issn 1753-9579
1753-9587
doi_str_mv 10.21314/jrmv.2019.206
publisher Infopro Digital Services Limited
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
url http://dx.doi.org/10.21314/jrmv.2019.206
document_store_str 0
active_str 0
description 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.
published_date 2019-06-30T14:28:09Z
_version_ 1821959614238818304
score 11.048149

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