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

Hyeoncheol Kim

doi:10.1007/3-540-44418-1_14

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

Hyeoncheol Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 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 language	English
Title of host publication	Discovery Science - 3rd International Conference, DS 2000, Proceedings
Editors	Setsuo Arikawa, Shinichi Morishita
Publisher	Springer Verlag
Pages	170-181
Number of pages	12
ISBN (Print)	9783540413523
DOIs	https://doi.org/10.1007/3-540-44418-1_14
Publication status	Published - 2000
Event	3rd International Conference on Discovery Science, DS 2000 - Kyoto, Japan Duration: 2000 Dec 4 → 2000 Dec 6

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1967
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	3rd International Conference on Discovery Science, DS 2000
Country/Territory	Japan
City	Kyoto
Period	00/12/4 → 00/12/6

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

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10.1007/3-540-44418-1_14

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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

Computationally efficient heuristics for if-then rule extraction from feed-forward neural networks. / Kim, Hyeoncheol.

Discovery Science - 3rd International Conference, DS 2000, Proceedings. ed. / Setsuo Arikawa; Shinichi Morishita. Springer Verlag, 2000. p. 170-181 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1967).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1967, Springer Verlag, pp. 170-181, 3rd International Conference on Discovery Science, DS 2000, Kyoto, Japan, 00/12/4. https://doi.org/10.1007/3-540-44418-1_14

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

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

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

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