An Efficient Technique for Extracting Fuzzy Rulesfrom Neural Networks
Artificial neural networks (ANN) have the ability to model input-output relationships from processing raw data. This characteristic makes them invaluable in industry domains where such knowledge is scarce at best. In the recent decades, in order to overcome the black-box characteristic of ANNs, researchers have attempted to extract the knowledge embedded within ANNs in the form of rules that can be used in inference systems. This paper presents a new technique that is able to extract a small set of rules from a two-layer ANN. The extracted rules yield high classification accuracy when implemented within a fuzzy inference system. The technique targets industry domains that possess less complex problems for which no expert knowledge exists and for which a simpler solution is preferred to a complex one. The proposed technique is more efficient, simple, and applicable than most of the previously proposed techniques.
[1] Andrews, R. Diederich, J., Tickle, A. Survey and Critique of
Techniques for Extracting Rules from Trained Artificial Neural
Networks. Knowledge-Based Systems, Volume 8, Number 6, pp. 373-
389. December 1995.
[2] Buckley, J.J., Hayashi, Y., Czogala, E. On the Equivalence of Neural
Nets and Fuzzy Expert Systems. Fuzzy Sets Systems, vol. 53, no. 2, pp.
129-134, 1993.
[3] Fu, L. Rule Generation from Neural Networks. IEEE Transactions on
Systems, Man, and Cybernetics, Volume 24, Number 8, pp. 1114-1124.
August 1994.
[4] Gallant, S. Connectionist Expert Systems. Communication of ACM,
Volume 31, pp. 152-169, 1988.
[5] Hayashi, Y. and A. Imura. Fuzzy Neural Expert System with
Automated Extraction of Fuzzy If-Then Rules from a Trained Neural
Network. Proceedings of First International Symposium on Uncertainty
Modeling and Analysis, pp. 489-494, 1990.
[6] Homik, K., Stinchcombe, M., White, H. Multilayer feedforward
networks are universal approximators. Neural Networks Archive,
Volume 2, Number 5, pp.359-366, 1989.
[7] Huang, S. and H. Xing. Extracting intelligible and concise fuzzy rules
from neural networks. Fuzzy Sets and Systems, Volume 132, pp. 233-
243. 2001.
[8] Kasabov, N. Learning Fuzzy Rules and Approximate Reasoning in
Fuzzy Neural Networks and Hybrid Systems. Fuzzy Sets and Systems,
Volume 82, pp. 135-149. 1996.
[9] Kasabov, N. Learning Fuzzy Rules through Neural Networks.
Proceedings of the 1st New Zealand International Two-Stream
Conference on Artificial Neural Networks and Expert Systems, 1993.
[10] Krishnan, R., Sivakumar, G., Bhattacharya, P. A Search Technique for
Rule Extraction from Trained Neural Networks. Patterns Recognition
Letters, Volume 20, pp. 273-280. 1999.
[11] Mitra, S. and Y. Hayashi. Neuro-Fuzzy Rule Generation: Survey in Soft
Computing Framework. IEEE Transactions on Neural Networks,
Volume 11, Number 3, pp.748-768. May 2000.
[12] Nauck, D. and R. Kruse. NEFCLASS - A Neuro-Fuzzy Approach for
the Classification of Data. Proceedings of the 1995 ACM Symposium on
Applied Computing, pp. 461-465, 1995.
[13] Nauck, D., Nauck, U., Kruse, R. Generating Classification Rules with
the Neuro-Fuzzy System NEFCLASS. Proceedings of the 1996 North
American Fuzzy Information Processing Society Conference, pp. 466-
470, 1996.
[14] Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998). UCI
Repository of Machine Learning Databases. Irvine, CA: University of
California, Department of Information and Computer Science.
http://www.ics.uci.edu/~mlearn/MLRepository.html
[15] Towell, G. and J. Shavlik. Extracting Refined Rules from Knowledge-
Based Neural Networks. Machine Learning, Volume 13, pp. 71-101,
1993.
[16] Towell, G., Shavlik, J., Noordewier, M.O. Refinement of Approximately
Correct Domain Theories by Knowledge-Based Neural Networks.
Proceedings of the Eigth National Conference on Artificial Intelligence,
pp.861-866, 1990.
[1] Andrews, R. Diederich, J., Tickle, A. Survey and Critique of
Techniques for Extracting Rules from Trained Artificial Neural
Networks. Knowledge-Based Systems, Volume 8, Number 6, pp. 373-
389. December 1995.
[2] Buckley, J.J., Hayashi, Y., Czogala, E. On the Equivalence of Neural
Nets and Fuzzy Expert Systems. Fuzzy Sets Systems, vol. 53, no. 2, pp.
129-134, 1993.
[3] Fu, L. Rule Generation from Neural Networks. IEEE Transactions on
Systems, Man, and Cybernetics, Volume 24, Number 8, pp. 1114-1124.
August 1994.
[4] Gallant, S. Connectionist Expert Systems. Communication of ACM,
Volume 31, pp. 152-169, 1988.
[5] Hayashi, Y. and A. Imura. Fuzzy Neural Expert System with
Automated Extraction of Fuzzy If-Then Rules from a Trained Neural
Network. Proceedings of First International Symposium on Uncertainty
Modeling and Analysis, pp. 489-494, 1990.
[6] Homik, K., Stinchcombe, M., White, H. Multilayer feedforward
networks are universal approximators. Neural Networks Archive,
Volume 2, Number 5, pp.359-366, 1989.
[7] Huang, S. and H. Xing. Extracting intelligible and concise fuzzy rules
from neural networks. Fuzzy Sets and Systems, Volume 132, pp. 233-
243. 2001.
[8] Kasabov, N. Learning Fuzzy Rules and Approximate Reasoning in
Fuzzy Neural Networks and Hybrid Systems. Fuzzy Sets and Systems,
Volume 82, pp. 135-149. 1996.
[9] Kasabov, N. Learning Fuzzy Rules through Neural Networks.
Proceedings of the 1st New Zealand International Two-Stream
Conference on Artificial Neural Networks and Expert Systems, 1993.
[10] Krishnan, R., Sivakumar, G., Bhattacharya, P. A Search Technique for
Rule Extraction from Trained Neural Networks. Patterns Recognition
Letters, Volume 20, pp. 273-280. 1999.
[11] Mitra, S. and Y. Hayashi. Neuro-Fuzzy Rule Generation: Survey in Soft
Computing Framework. IEEE Transactions on Neural Networks,
Volume 11, Number 3, pp.748-768. May 2000.
[12] Nauck, D. and R. Kruse. NEFCLASS - A Neuro-Fuzzy Approach for
the Classification of Data. Proceedings of the 1995 ACM Symposium on
Applied Computing, pp. 461-465, 1995.
[13] Nauck, D., Nauck, U., Kruse, R. Generating Classification Rules with
the Neuro-Fuzzy System NEFCLASS. Proceedings of the 1996 North
American Fuzzy Information Processing Society Conference, pp. 466-
470, 1996.
[14] Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998). UCI
Repository of Machine Learning Databases. Irvine, CA: University of
California, Department of Information and Computer Science.
http://www.ics.uci.edu/~mlearn/MLRepository.html
[15] Towell, G. and J. Shavlik. Extracting Refined Rules from Knowledge-
Based Neural Networks. Machine Learning, Volume 13, pp. 71-101,
1993.
[16] Towell, G., Shavlik, J., Noordewier, M.O. Refinement of Approximately
Correct Domain Theories by Knowledge-Based Neural Networks.
Proceedings of the Eigth National Conference on Artificial Intelligence,
pp.861-866, 1990.
@article{"International Journal of Information, Control and Computer Sciences:64785", author = "Besa Muslimi and Miriam A. M. Capretz and Jagath Samarabandu", title = "An Efficient Technique for Extracting Fuzzy Rulesfrom Neural Networks", abstract = "Artificial neural networks (ANN) have the ability to model input-output relationships from processing raw data. This characteristic makes them invaluable in industry domains where such knowledge is scarce at best. In the recent decades, in order to overcome the black-box characteristic of ANNs, researchers have attempted to extract the knowledge embedded within ANNs in the form of rules that can be used in inference systems. This paper presents a new technique that is able to extract a small set of rules from a two-layer ANN. The extracted rules yield high classification accuracy when implemented within a fuzzy inference system. The technique targets industry domains that possess less complex problems for which no expert knowledge exists and for which a simpler solution is preferred to a complex one. The proposed technique is more efficient, simple, and applicable than most of the previously proposed techniques.
", keywords = "fuzzy rule extraction, fuzzy systems, knowledgeacquisition, pattern recognition, artificial neural networks.", volume = "2", number = "4", pages = "1331-7", }
