Knowledge incorporation and rule extraction in neural networks

Minoru Fukumi, Yasue Mitsukura, Norio Akamatsu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper a new knowledge incorporation and rule extraction method in neural networks is presented. The rule form of an if–then type can be inserted into a neural network (NN) as knowledge of a problem. NN is then trained by using a set of training samples. In this case the structure learning algorithm with forgetting is used to generate a small-sized NN system. After the NN training, rules are extracted from it. The results of computer simulations show that this approach can generate obvious network architectures and as a result simple rules compared with conventional rule extraction methods.

Original languageEnglish
Title of host publicationArtificial Neural Networks - ICANN 2001 - International Conference, Proceedings
EditorsKurt Hornik, Georg Dorffner, Horst Bischof
PublisherSpringer Verlag
Pages1248-1253
Number of pages6
ISBN (Print)3540424865, 9783540446682
DOIs
Publication statusPublished - 2001 Jan 1
EventInternational Conference on Artificial Neural Networks, ICANN 2001 - Vienna, Austria
Duration: 2001 Aug 212001 Aug 25

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2130
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Conference on Artificial Neural Networks, ICANN 2001
CountryAustria
CityVienna
Period01/8/2101/8/25

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Fukumi, M., Mitsukura, Y., & Akamatsu, N. (2001). Knowledge incorporation and rule extraction in neural networks. In K. Hornik, G. Dorffner, & H. Bischof (Eds.), Artificial Neural Networks - ICANN 2001 - International Conference, Proceedings (pp. 1248-1253). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2130). Springer Verlag. https://doi.org/10.1007/3-540-44668-0_174