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Comparing a Genetic Fuzzy and a Neurofuzzy Classifier for Credit Scoring

Comparing a Genetic Fuzzy and a Neurofuzzy Classifier for Credit Scoring
Comparing a Genetic Fuzzy and a Neurofuzzy Classifier for Credit Scoring
In this paper, we evaluate and contrast two types of fuzzy classifiers for credit scoring. The first classifier uses evolutionary optimization and boosting for learning fuzzy classification rules. The second classifier is a fuzzy neural network that employs a fuzzy variant of the classic backpropagation learning algorithm. The experiments are carried out on a real life credit scoring data set. It is shown that, for the case at hand, the boosted genetic fuzzy classifier performs better than both the neurofuzzy classifier and the well-known C4.5(rules) decision tree(rules) induction algorithm. However, the better performance of the genetic fuzzy classifier is offset by the fact that it infers approximate fuzzy rules which are less comprehensible for humans than the descriptive fuzzy rules inferred by the neurofuzzy classifier.
1067-1083
Hoffmann, F.
2e0f0f10-4833-4c77-ad32-7512dede38bd
Baesens, B.
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Martens, J.
57276ca7-9b7a-4244-8c29-0610074d3806
Put, F.
ef1a23c1-b392-4f01-a812-ee1b068f021f
Vanthienen, J.
808131f1-b77b-4dee-bdda-90a94124c999
Hoffmann, F.
2e0f0f10-4833-4c77-ad32-7512dede38bd
Baesens, B.
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Martens, J.
57276ca7-9b7a-4244-8c29-0610074d3806
Put, F.
ef1a23c1-b392-4f01-a812-ee1b068f021f
Vanthienen, J.
808131f1-b77b-4dee-bdda-90a94124c999

Hoffmann, F., Baesens, B., Martens, J., Put, F. and Vanthienen, J. (2002) Comparing a Genetic Fuzzy and a Neurofuzzy Classifier for Credit Scoring. International Journal of Intelligent Systems, 17 (11), 1067-1083. (doi:10.1002/int.10052).

Record type: Article

Abstract

In this paper, we evaluate and contrast two types of fuzzy classifiers for credit scoring. The first classifier uses evolutionary optimization and boosting for learning fuzzy classification rules. The second classifier is a fuzzy neural network that employs a fuzzy variant of the classic backpropagation learning algorithm. The experiments are carried out on a real life credit scoring data set. It is shown that, for the case at hand, the boosted genetic fuzzy classifier performs better than both the neurofuzzy classifier and the well-known C4.5(rules) decision tree(rules) induction algorithm. However, the better performance of the genetic fuzzy classifier is offset by the fact that it infers approximate fuzzy rules which are less comprehensible for humans than the descriptive fuzzy rules inferred by the neurofuzzy classifier.

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Published date: 2002
Organisations: Management

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Local EPrints ID: 36737
URI: http://eprints.soton.ac.uk/id/eprint/36737
PURE UUID: 88dc9dc3-ddd9-4e77-b95a-78194ad49543
ORCID for B. Baesens: ORCID iD orcid.org/0000-0002-5831-5668

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Date deposited: 24 May 2006
Last modified: 16 Mar 2024 03:39

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Contributors

Author: F. Hoffmann
Author: B. Baesens ORCID iD
Author: J. Martens
Author: F. Put
Author: J. Vanthienen

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