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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-04

AUTHORS

Chao-Ton Su, Yan-Cheng Chen

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. More... »

PAGES

645-658

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00500-011-0762-8

DOI

http://dx.doi.org/10.1007/s00500-011-0762-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1044615210


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