Knowledge incorporation and rule extraction in neural networks

Minoru Fukumi; Yasue Mitsukura; Norio Akamatsu

doi:10.1007/3-540-44668-0_174

Knowledge incorporation and rule extraction in neural networks

Minoru Fukumi, Yasue Mitsukura, Norio Akamatsu

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

1 Citation (Scopus)

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 language	English
Title of host publication	Artificial Neural Networks - ICANN 2001 - International Conference, Proceedings
Editors	Kurt Hornik, Georg Dorffner, Horst Bischof
Publisher	Springer Verlag
Pages	1248-1253
Number of pages	6
ISBN (Print)	3540424865, 9783540446682
DOIs	https://doi.org/10.1007/3-540-44668-0_174
Publication status	Published - 2001
Externally published	Yes
Event	International Conference on Artificial Neural Networks, ICANN 2001 - Vienna, Austria Duration: 2001 Aug 21 → 2001 Aug 25

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2130
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	International Conference on Artificial Neural Networks, ICANN 2001
Country/Territory	Austria
City	Vienna
Period	01/8/21 → 01/8/25

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/3-540-44668-0_174

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

Knowledge incorporation and rule extraction in neural networks. / Fukumi, Minoru; Mitsukura, Yasue; Akamatsu, Norio.

Artificial Neural Networks - ICANN 2001 - International Conference, Proceedings. ed. / Kurt Hornik; Georg Dorffner; Horst Bischof. Springer Verlag, 2001. p. 1248-1253 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2130).

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

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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2130, Springer Verlag, pp. 1248-1253, International Conference on Artificial Neural Networks, ICANN 2001, Vienna, Austria, 01/8/21. https://doi.org/10.1007/3-540-44668-0_174

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

Fukumi, Minoru ; Mitsukura, Yasue ; Akamatsu, Norio. / Knowledge incorporation and rule extraction in neural networks. Artificial Neural Networks - ICANN 2001 - International Conference, Proceedings. editor / Kurt Hornik ; Georg Dorffner ; Horst Bischof. Springer Verlag, 2001. pp. 1248-1253 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{c8ed669d24a34dfd917a52c80b823101,

title = "Knowledge incorporation and rule extraction in neural networks",

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.",

author = "Minoru Fukumi and Yasue Mitsukura and Norio Akamatsu",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2001.; International Conference on Artificial Neural Networks, ICANN 2001 ; Conference date: 21-08-2001 Through 25-08-2001",

year = "2001",

doi = "10.1007/3-540-44668-0_174",

language = "English",

isbn = "3540424865",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "1248--1253",

editor = "Kurt Hornik and Georg Dorffner and Horst Bischof",

booktitle = "Artificial Neural Networks - ICANN 2001 - International Conference, Proceedings",

}

TY - GEN

T1 - Knowledge incorporation and rule extraction in neural networks

AU - Fukumi, Minoru

AU - Mitsukura, Yasue

AU - Akamatsu, Norio

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2001.

PY - 2001

Y1 - 2001

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84959020333&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84959020333&partnerID=8YFLogxK

U2 - 10.1007/3-540-44668-0_174

DO - 10.1007/3-540-44668-0_174

M3 - Conference contribution

AN - SCOPUS:84959020333

SN - 3540424865

SN - 9783540446682

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 1248

EP - 1253

BT - Artificial Neural Networks - ICANN 2001 - International Conference, Proceedings

A2 - Hornik, Kurt

A2 - Dorffner, Georg

A2 - Bischof, Horst

PB - Springer Verlag

T2 - International Conference on Artificial Neural Networks, ICANN 2001

Y2 - 21 August 2001 through 25 August 2001

ER -

Knowledge incorporation and rule extraction in neural networks

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this