The University of Southampton
University of Southampton Institutional Repository

Integrated framework for profit-based feature selection and SVM classification in credit scoring

Integrated framework for profit-based feature selection and SVM classification in credit scoring
Integrated framework for profit-based feature selection and SVM classification in credit scoring
n this paper, we propose a profit-driven approach for classifier construction and simultaneous variable selection based on linear Support Vector Machines. The main goal is to incorporate business-related information such as the variable acquisition costs, the Type I and II error costs, and the profit generated by correctly classified instances, into the modeling process. Our proposal incorporates a group penalty function in the SVM formulation in order to penalize the variables simultaneously that belong to the same group, assuming that companies often acquire groups of related variables for a given cost rather than acquiring them individually. The proposed framework was studied in a credit scoring problem for a Chilean bank, and led to superior performance with respect to business-related goals.
Profit measure, Group penalty, Credit scoring, Support Vector Machines, Analytics
0167-9236
113-121
Maldonado, Sebastián
9e5fb121-d905-4337-beb3-bba6f7da9ae2
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
López, Julio
14edc460-148d-48b1-b415-6e2c1c511455
Pérez, Juan
6f8b9b90-b3e6-4b03-b444-35e32d9fb3f9
Maldonado, Sebastián
9e5fb121-d905-4337-beb3-bba6f7da9ae2
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
López, Julio
14edc460-148d-48b1-b415-6e2c1c511455
Pérez, Juan
6f8b9b90-b3e6-4b03-b444-35e32d9fb3f9

Maldonado, Sebastián, Bravo, Cristian, López, Julio and Pérez, Juan (2017) Integrated framework for profit-based feature selection and SVM classification in credit scoring. Decision Support Systems, 104, 113-121. (doi:10.1016/j.dss.2017.10.007).

Record type: Article

Abstract

n this paper, we propose a profit-driven approach for classifier construction and simultaneous variable selection based on linear Support Vector Machines. The main goal is to incorporate business-related information such as the variable acquisition costs, the Type I and II error costs, and the profit generated by correctly classified instances, into the modeling process. Our proposal incorporates a group penalty function in the SVM formulation in order to penalize the variables simultaneously that belong to the same group, assuming that companies often acquire groups of related variables for a given cost rather than acquiring them individually. The proposed framework was studied in a credit scoring problem for a Chilean bank, and led to superior performance with respect to business-related goals.

Text
Self-Archive Version - Accepted Manuscript
Download (404kB)

More information

Accepted/In Press date: 15 October 2017
e-pub ahead of print date: 18 October 2017
Published date: December 2017
Keywords: Profit measure, Group penalty, Credit scoring, Support Vector Machines, Analytics

Identifiers

Local EPrints ID: 414994
URI: https://eprints.soton.ac.uk/id/eprint/414994
ISSN: 0167-9236
PURE UUID: 78960638-8769-41d7-9b59-3899f3443fca
ORCID for Cristian Bravo: ORCID iD orcid.org/0000-0003-1579-1565

Catalogue record

Date deposited: 20 Oct 2017 16:31
Last modified: 17 Sep 2019 04:37

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×