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
PublisherSpringer Verlag
Pages170-181
Number of pages12
Volume1967
ISBN (Print)9783540413523
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)03029743
ISSN (Electronic)16113349

Other

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

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Kim, H. (2000). Computationally efficient heuristics for if-then rule extraction from feed-forward neural networks. In Discovery Science - 3rd International Conference, DS 2000, Proceedings (Vol. 1967, pp. 170-181). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1967). Springer Verlag.