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Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms

Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms
Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms
Generating both accurate as well as explanatory classification rules is becoming increasingly important in a knowledge discovery context. In this paper, we investigate the power and usefulness of fuzzy classification rules for data mining purposes. We propose two evolutionary fuzzy rule learners: an evolution strategy that generates approximate fuzzy rules, whereby each rule has its own specific definition of membership functions, and a genetic algorithm that extracts descriptive fuzzy rules, where all fuzzy rules share a common, linguistically interpretable definition of membership functions in disjunctive normal form. The performance of the evolutionary fuzzy rule learners is compared with that of Nefclass, a neurofuzzy classifier, and a selection of other well-known classification algorithms on a number of publicly available data sets and two real life Benelux financial credit scoring data sets. It is shown that the genetic fuzzy classifiers compare favourably with the other classifiers in terms of classification accuracy. Furthermore, the approximate and descriptive fuzzy rules yield about the same classification accuracy across the different data sets
fuzzy sets, credit scoring, data mining, classification
0377-2217
540-555
Hoffmann, F.
2e0f0f10-4833-4c77-ad32-7512dede38bd
Baesens, B.
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Mues, C.
07438e46-bad6-48ba-8f56-f945bc2ff934
Vanthienen, J.
808131f1-b77b-4dee-bdda-90a94124c999
Hoffmann, F.
2e0f0f10-4833-4c77-ad32-7512dede38bd
Baesens, B.
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Mues, C.
07438e46-bad6-48ba-8f56-f945bc2ff934
Vanthienen, J.
808131f1-b77b-4dee-bdda-90a94124c999

Hoffmann, F., Baesens, B., Mues, C. and Vanthienen, J. (2007) Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms. European Journal of Operational Research, 177 (1), 540-555. (doi:10.1016/j.ejor.2005.09.044).

Record type: Article

Abstract

Generating both accurate as well as explanatory classification rules is becoming increasingly important in a knowledge discovery context. In this paper, we investigate the power and usefulness of fuzzy classification rules for data mining purposes. We propose two evolutionary fuzzy rule learners: an evolution strategy that generates approximate fuzzy rules, whereby each rule has its own specific definition of membership functions, and a genetic algorithm that extracts descriptive fuzzy rules, where all fuzzy rules share a common, linguistically interpretable definition of membership functions in disjunctive normal form. The performance of the evolutionary fuzzy rule learners is compared with that of Nefclass, a neurofuzzy classifier, and a selection of other well-known classification algorithms on a number of publicly available data sets and two real life Benelux financial credit scoring data sets. It is shown that the genetic fuzzy classifiers compare favourably with the other classifiers in terms of classification accuracy. Furthermore, the approximate and descriptive fuzzy rules yield about the same classification accuracy across the different data sets

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More information

Published date: February 2007
Keywords: fuzzy sets, credit scoring, data mining, classification

Identifiers

Local EPrints ID: 37167
URI: http://eprints.soton.ac.uk/id/eprint/37167
ISSN: 0377-2217
PURE UUID: 7c2f0a8b-b388-48a5-a485-1e6217af6b35
ORCID for B. Baesens: ORCID iD orcid.org/0000-0002-5831-5668
ORCID for C. Mues: ORCID iD orcid.org/0000-0002-6289-5490

Catalogue record

Date deposited: 10 Jul 2006
Last modified: 16 Mar 2024 03:40

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Contributors

Author: F. Hoffmann
Author: B. Baesens ORCID iD
Author: C. Mues ORCID iD
Author: J. Vanthienen

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