Designing a decompositional rule extraction algorithm for neural networks

Jen Cheng Chen; Jia Sheng Heh; Maiga Chang

doi:10.1007/11759966_194

Designing a decompositional rule extraction algorithm for neural networks

Jen Cheng Chen, Jia Sheng Heh, Maiga Chang

Chung Yuan Christian University

Research output: Chapter in Book/Report/Conference proceeding › Published Conference contribution › peer-review

1 Citation (Scopus)

Abstract

The neural networks are successfully applied to many applications in different domains. However, due to the results made by the neural networks are difficult to explain the decision process of neural networks is supposed as a black box. The explanation of reasoning is important to some applications such like credit approval application and medical diagnosing software. Therefore, the rule extraction algorithm is becoming more and more important in explaining the extracted rules from the neural networks. In this paper, a decompositional algorithm is analyzed and designed to extract rules from neural networks. The algorithm is simple but efficient; can reduce the extracted rules but improve the efficiency of the algorithm at the same time. Moreover, the algorithm is compared to the other two algorithms, M-of-N and Garcez, by solving the MONK's problem.

Original language	English
Title of host publication	Advances in Neural Networks - ISNN 2006
Subtitle of host publication	Third International Symposium on Neural Networks, ISNN 2006, Proceedings
Pages	1305-1311
Number of pages	7
DOIs	https://doi.org/10.1007/11759966_194
Publication status	Published - 2006
Event	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, China Duration: 28 May 2006 → 1 Jun. 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3971 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
Country/Territory	China
City	Chengdu
Period	28/05/06 → 1/06/06

Access to Document

10.1007/11759966_194

Cite this

Chen, J. C., Heh, J. S., & Chang, M. (2006). Designing a decompositional rule extraction algorithm for neural networks. In Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings (pp. 1305-1311). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3971 LNCS). https://doi.org/10.1007/11759966_194

Chen, Jen Cheng ; Heh, Jia Sheng ; Chang, Maiga. / Designing a decompositional rule extraction algorithm for neural networks. Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings. 2006. pp. 1305-1311 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{c8159e73e045488a87a90e32dd47fca7,

title = "Designing a decompositional rule extraction algorithm for neural networks",

abstract = "The neural networks are successfully applied to many applications in different domains. However, due to the results made by the neural networks are difficult to explain the decision process of neural networks is supposed as a black box. The explanation of reasoning is important to some applications such like credit approval application and medical diagnosing software. Therefore, the rule extraction algorithm is becoming more and more important in explaining the extracted rules from the neural networks. In this paper, a decompositional algorithm is analyzed and designed to extract rules from neural networks. The algorithm is simple but efficient; can reduce the extracted rules but improve the efficiency of the algorithm at the same time. Moreover, the algorithm is compared to the other two algorithms, M-of-N and Garcez, by solving the MONK's problem.",

author = "Chen, {Jen Cheng} and Heh, {Jia Sheng} and Maiga Chang",

year = "2006",

doi = "10.1007/11759966_194",

language = "English",

isbn = "354034439X",

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

pages = "1305--1311",

booktitle = "Advances in Neural Networks - ISNN 2006",

note = "3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks ; Conference date: 28-05-2006 Through 01-06-2006",

}

Chen, JC, Heh, JS & Chang, M 2006, Designing a decompositional rule extraction algorithm for neural networks. in Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3971 LNCS, pp. 1305-1311, 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks, Chengdu, China, 28/05/06. https://doi.org/10.1007/11759966_194

Designing a decompositional rule extraction algorithm for neural networks. / Chen, Jen Cheng; Heh, Jia Sheng; Chang, Maiga.
Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings. 2006. p. 1305-1311 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3971 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Published Conference contribution › peer-review

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AB - The neural networks are successfully applied to many applications in different domains. However, due to the results made by the neural networks are difficult to explain the decision process of neural networks is supposed as a black box. The explanation of reasoning is important to some applications such like credit approval application and medical diagnosing software. Therefore, the rule extraction algorithm is becoming more and more important in explaining the extracted rules from the neural networks. In this paper, a decompositional algorithm is analyzed and designed to extract rules from neural networks. The algorithm is simple but efficient; can reduce the extracted rules but improve the efficiency of the algorithm at the same time. Moreover, the algorithm is compared to the other two algorithms, M-of-N and Garcez, by solving the MONK's problem.

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

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Chen JC, Heh JS, Chang M. Designing a decompositional rule extraction algorithm for neural networks. In Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings. 2006. p. 1305-1311. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11759966_194

Designing a decompositional rule extraction algorithm for neural networks

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