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Soft Computing

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01.04.2012 | Original Paper

Rule extraction algorithm from support vector machines and its application to credit screening

verfasst von: Chao-Ton Su, Yan-Cheng Chen

Erschienen in: Soft Computing | Ausgabe 4/2012

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Abstract

Developing rule extraction algorithms from machine learning techniques such as artificial neural networks and support vector machines (SVMs), which are considered incomprehensible black-box models, is an important topic in current research. This study proposes a rule extraction algorithm from SVMs that uses a kernel-based clustering algorithm to integrate all support vectors and genetic algorithms into extracted rule sets. This study uses measurements of accuracy, sensitivity, specificity, coverage, fidelity and comprehensibility to evaluate the performance of the proposed method on the public credit screening data sets. Results indicate that the proposed method performs better than other rule extraction algorithms. Thus, the proposed algorithm is an essential analysis tool that can be effectively used in data mining fields.

Vorheriger Artikel On normal-valued basic pseudo-hoops

Nächster Artikel A generalization of the Chebyshev type inequalities for Sugeno integrals

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Titel: Rule extraction algorithm from support vector machines and its application to credit screening
verfasst von: Chao-Ton Su
Yan-Cheng Chen
Publikationsdatum: 01.04.2012
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 4/2012
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-011-0762-8

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