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.
9812380663
355-362
Hoffman, F.
9dd551f8-31a5-4f8d-827f-eef4868f39ad
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
2002
Hoffman, F.
9dd551f8-31a5-4f8d-827f-eef4868f39ad
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
Hoffman, F., Baesens, B., Martens, J., Put, F. and Vanthienen, J.
(2002)
Comparing a genetic fuzzy and a neurofuzzy classifier for credit scoring.
Da Ruan, Pierre D'hont and Kerre, Etienne E.
(eds.)
In Computational Intelligent Systems for Applied Research. Proceedings of the 5th International FLINS Conference.
World Scientific.
.
(doi:10.1002/int.10052).
Record type:
Conference or Workshop Item
(Paper)
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
Additional Information:
also available as an article in International Journal of Intelligent Systems (see alternative location)
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Proceedings of the 5th International FLINS Conference, Gent, Belgium, 2002-09-16 - 2002-09-18
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Local EPrints ID: 37154
URI: http://eprints.soton.ac.uk/id/eprint/37154
ISBN: 9812380663
PURE UUID: 1a1dd6c7-ed5e-4abe-b74f-361cf055e56f
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Date deposited: 26 May 2006
Last modified: 16 Mar 2024 03:39
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Contributors
Author:
F. Hoffman
Author:
J. Martens
Author:
F. Put
Author:
J. Vanthienen
Editor:
Pierre D'hont Da Ruan
Editor:
Etienne E. Kerre
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