Computationally efficient heuristics for if-then rule extraction from feed-forward neural networks

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

8 Citations (Scopus)

Abstract

In this paper, we address computational complexity issues of decompositional approaches to if-then rule extraction from feed-forward neural networks. We also introduce a computationally efficient technique based on ordered-attributes. It reduces search space significantly and finds valid and general rules for single nodes in the networks. Empirical results are shown.

Original languageEnglish
Title of host publicationDiscovery Science - 3rd International Conference, DS 2000, Proceedings
EditorsSetsuo Arikawa, Shinichi Morishita
PublisherSpringer Verlag
Pages170-181
Number of pages12
ISBN (Print)9783540413523
DOIs
Publication statusPublished - 2000
Event3rd International Conference on Discovery Science, DS 2000 - Kyoto, Japan
Duration: 2000 Dec 42000 Dec 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1967
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Conference on Discovery Science, DS 2000
CountryJapan
CityKyoto
Period00/12/400/12/6

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Kim, H. (2000). Computationally efficient heuristics for if-then rule extraction from feed-forward neural networks. In S. Arikawa, & S. Morishita (Eds.), Discovery Science - 3rd International Conference, DS 2000, Proceedings (pp. 170-181). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1967). Springer Verlag. https://doi.org/10.1007/3-540-44418-1_14