Journal article 222 views
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
Full text not available from this repository: check for access using links below.
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...
| Published in: | Journal of Risk Model Validation |
|---|---|
| ISSN: | 1753-9579 1753-9587 |
| Published: |
Infopro Digital Services Limited
2019
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa64258 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| 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 |