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A comprehensible SOM-based scoring system

A comprehensible SOM-based scoring system
A comprehensible SOM-based scoring system
The significant growth of consumer credit has resulted in a wide range of statistical and non-statistical methods for classifying applicants in ‘good’ and ‘bad’ risk categories. Traditionally, (logistic) regression used to be one of the most popular methods for this task, but recently some newer techniques like neural networks and support vector machines have shown excellent classification performance. Self-organizing maps (SOMs) have existed for decades and although they have been used in various application areas, only little research has been done to investigate their appropriateness for credit scoring. In this paper, it is shown how a trained SOM can be used for classification and how the basic SOM-algorithm can be integrated with supervised techniques like the multi-layered perceptron. Classification accuracy of the models is benchmarked with results reported previously.
0302-9743
80-89
Huysmans, Johan
0a2bb876-e5bc-42c5-bcf1-18f089a6eec3
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Vanthienen, Jan
6f3d818f-0fce-46fa-966b-160e645caf6d
Huysmans, Johan
0a2bb876-e5bc-42c5-bcf1-18f089a6eec3
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Vanthienen, Jan
6f3d818f-0fce-46fa-966b-160e645caf6d

Huysmans, Johan, Baesens, Bart and Vanthienen, Jan (2005) A comprehensible SOM-based scoring system. Lecture Notes in Computer Science, 3587, 80-89.

Record type: Article

Abstract

The significant growth of consumer credit has resulted in a wide range of statistical and non-statistical methods for classifying applicants in ‘good’ and ‘bad’ risk categories. Traditionally, (logistic) regression used to be one of the most popular methods for this task, but recently some newer techniques like neural networks and support vector machines have shown excellent classification performance. Self-organizing maps (SOMs) have existed for decades and although they have been used in various application areas, only little research has been done to investigate their appropriateness for credit scoring. In this paper, it is shown how a trained SOM can be used for classification and how the basic SOM-algorithm can be integrated with supervised techniques like the multi-layered perceptron. Classification accuracy of the models is benchmarked with results reported previously.

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Published date: 2005

Identifiers

Local EPrints ID: 36741
URI: http://eprints.soton.ac.uk/id/eprint/36741
ISSN: 0302-9743
PURE UUID: 50507d0c-bf5c-4b68-ae77-754d7e0309cb
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

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Date deposited: 11 Jul 2006
Last modified: 03 Dec 2019 01:48

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Contributors

Author: Johan Huysmans
Author: Bart Baesens ORCID iD
Author: Jan Vanthienen

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