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Filter- versus wrapper-based feature selection for credit scoring

Filter- versus wrapper-based feature selection for credit scoring
Filter- versus wrapper-based feature selection for credit scoring
We address the problem of credit scoring as a classification and feature subset selection problem. Based on the current framework of sophisticated feature selection methods, we identify features that contain the most relevant information to distinguish good loan payers from bad loan payers. The feature selection methods are validated on several real-world datasets with different types of classifiers. We show the advantages following from using the subspace approach to classification. We discuss many practical issues related to the applicability of feature selection methods. We show and discuss some difficulties that used to be insufficiently emphasized in standard feature selection literature.
985-999
Somol, Petr
7fecfdfc-b81a-4213-b407-bf2c0c7a90a3
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Pudil, Pavel
bb998b26-e15d-4fcd-a5e6-32b5c8b22cae
Vanthienen, Jan
6f3d818f-0fce-46fa-966b-160e645caf6d
Somol, Petr
7fecfdfc-b81a-4213-b407-bf2c0c7a90a3
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Pudil, Pavel
bb998b26-e15d-4fcd-a5e6-32b5c8b22cae
Vanthienen, Jan
6f3d818f-0fce-46fa-966b-160e645caf6d

Somol, Petr, Baesens, Bart, Pudil, Pavel and Vanthienen, Jan (2005) Filter- versus wrapper-based feature selection for credit scoring. International Journal of Intelligent Systems, 20 (10), 985-999. (doi:10.1002/int.20103).

Record type: Article

Abstract

We address the problem of credit scoring as a classification and feature subset selection problem. Based on the current framework of sophisticated feature selection methods, we identify features that contain the most relevant information to distinguish good loan payers from bad loan payers. The feature selection methods are validated on several real-world datasets with different types of classifiers. We show the advantages following from using the subspace approach to classification. We discuss many practical issues related to the applicability of feature selection methods. We show and discuss some difficulties that used to be insufficiently emphasized in standard feature selection literature.

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Published date: 2005

Identifiers

Local EPrints ID: 36732
URI: http://eprints.soton.ac.uk/id/eprint/36732
PURE UUID: 2f5f7445-948d-41b3-8f96-1c6e346f8f8c
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

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Date deposited: 11 Jul 2006
Last modified: 19 Nov 2019 01:47

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Contributors

Author: Petr Somol
Author: Bart Baesens ORCID iD
Author: Pavel Pudil
Author: Jan Vanthienen

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