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A multi-objective approach for profit-driven feature selection in credit scoring

A multi-objective approach for profit-driven feature selection in credit scoring
A multi-objective approach for profit-driven feature selection in credit scoring
In credit scoring, feature selection aims at removing irrelevant data to improve the performance of the scorecard and its interpretability. Standard techniques treat feature selection as a single-objective task and rely on statistical criteria such as correlation. Recent studies suggest that using profit-based indicators may improve the quality of scoring models for businesses. We extend the use of profit measures to feature selection and develop a multi-objective wrapper framework based on the NSGA-II genetic algorithm with two fitness functions: the Expected Maximum Profit (EMP) and the number of features. Experiments on multiple credit scoring data sets demonstrate that the proposed approach develops scorecards that can yield a higher expected profit using fewer features than conventional feature selection strategies.
0167-9236
106-117
Kozodoi, Nikita
ac0299e9-8c56-474e-9d7f-88dfc9b4f2b9
Lessmann, Stefan
3b9f8133-67bb-4bcc-9183-e1a5db294b01
Papakonstantinou, Konstantinos
e2a37693-a781-452e-b9de-4bbb2aa89ce2
Gatsoulis, Yiannis
aa32abf6-4366-4108-9239-f603c67b0189
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Kozodoi, Nikita
ac0299e9-8c56-474e-9d7f-88dfc9b4f2b9
Lessmann, Stefan
3b9f8133-67bb-4bcc-9183-e1a5db294b01
Papakonstantinou, Konstantinos
e2a37693-a781-452e-b9de-4bbb2aa89ce2
Gatsoulis, Yiannis
aa32abf6-4366-4108-9239-f603c67b0189
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0

Kozodoi, Nikita, Lessmann, Stefan, Papakonstantinou, Konstantinos, Gatsoulis, Yiannis and Baesens, Bart (2019) A multi-objective approach for profit-driven feature selection in credit scoring. Decision Support Systems, 120, 106-117. (doi:10.1016/j.dss.2019.03.011).

Record type: Article

Abstract

In credit scoring, feature selection aims at removing irrelevant data to improve the performance of the scorecard and its interpretability. Standard techniques treat feature selection as a single-objective task and rely on statistical criteria such as correlation. Recent studies suggest that using profit-based indicators may improve the quality of scoring models for businesses. We extend the use of profit measures to feature selection and develop a multi-objective wrapper framework based on the NSGA-II genetic algorithm with two fitness functions: the Expected Maximum Profit (EMP) and the number of features. Experiments on multiple credit scoring data sets demonstrate that the proposed approach develops scorecards that can yield a higher expected profit using fewer features than conventional feature selection strategies.

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More information

Accepted/In Press date: 27 March 2019
e-pub ahead of print date: 4 April 2019
Published date: May 2019

Identifiers

Local EPrints ID: 431615
URI: https://eprints.soton.ac.uk/id/eprint/431615
ISSN: 0167-9236
PURE UUID: 188522cd-270e-426d-8973-36331e7a3522
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

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Date deposited: 11 Jun 2019 16:30
Last modified: 03 Dec 2019 01:48

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