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.
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References
Andrews, R., Diederich, J., Tickle, A.B.: Survey and Critique of Techniques for Extracting rules from Trained Artificial Neural Networks. Knowledge Base Systems 8(6), 373–389 (1995)
Andrews, R., Geva, S.: Inserting and Extracting Knowledge from Constrained Error Back Propagation Networks. In: Proc. of 6th Australian Conference on Neural Networks, Sydney, NSW (1995)
Andrews, R., Geva, S.: Rule Extraction from Local Cluster Neural Nets. Neurocomputing 47, 1–20 (2002)
Chang, M., Chen, J.C., Chang, J.W., Heh, J.S.: Advanced Process Control Expert System of CVD Membrane Thickness Based on Neural Network. Materials Science Forum 505-507, 313–318 (2006)
Chen, J.C., Liu, T.S., Weng, C.S., Heh, J.S.: An Expert System of Coronary Artery Disease in Chinese and Western Medicine. In: Proc. of 6th Asian-Pacific Conference on Medical and Biological Engineering, Tsukuba, Japan, April 24-27 (2005) PA-3-06
Craven, M.W., Shavlik, J.W.: Using Sampling and Queries to Extract Rules from Trained Neural Networks. In: Proc. of 11th International Conference on Machine Learning, New Brunswick, pp. 37–45 (1994)
Freeman, J.A., Skapura, D.M.: Neural Networks. Addison-Wesley, New York (1992)
Fu, L.M.: Rule Generation from Neural Networks. IEEE Transactions on Systems, Man, and Cybernetics 28(8), 1114–1124 (1994)
Garcez, A.S., d’Avila, B.K., Gabbay, D.M.: Symbolic Knowledge Extraction from Trained Neural Networks: A Sound Approach. Artificial Intelligence 125, 155–207 (2001)
Jackson, P.: Introduction to Expert Systems. Addison-Wesley, New York (1999)
Krishnan, R., Sivakumar, G., Bhattacharya, P.: A Search Technique for Rule Extraction from Trained Neural Networks. Pattern Recognition Letters 20, 273–280 (1999)
Pop, E., Hayward, R., Diedrich, A.: RULENEG: Extracting Rules from a Trained Neural Network Using Stepwise Negation. Technical Report, Neurocomputing Research Centre, Queensland University of Technology (1994)
Setiono, R.: A Penalty Function Approach for Pruning Feedforward Neural Networks. Neural Computation 9(1), 185–204 (1997)
Setiono, R.: Extracting Rules from Neural Networks by Pruning and Hidden-unit Splitting. Neural Computation 9(1), 205–225 (1997)
Taha, I.A., Ghosh, J.: Symbolic Interpretation of Artificial Neural Networks. IEEE transactions on Knowledge and Data Engineering 11(3), 448–463 (1999)
Thrun, S.B. (ed.): The MONK’s Problems: A Performance Comparison of Different Learning Algorithms. Technical Report CMU-CS-91-197. Carnegie Mellon University (1991)
Thrun, S.B.: Extracting Provably Correct Rules from Artificial Neural Networks. Technical Report IAI-TR-93-5, Institut für Informatik III. University of Bonn, Germany (1994)
Tickle, A.B., Andrews, R., Golea, M., Diederich, J.: The Truth Will Come to Light: Directions and Challenges in Extracting the Knowledge Embedded within Trained Artificial Neural Networks. IEEE Trans. Neural Networks 9(6), 1057–1068 (1998)
Towell, G.G., Shavlik, J.W.: The Extraction of Refined Rules from Knowledge-Based Neural Networks. Machine Learning 13, 71–101 (1993)
Towell, G.G., Shavlik, J.W.: Knowledge-Based Artificial Neural Networks. Artificial Intelligence 70, 119–165 (1994)
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Chen, JC., Heh, JS., Chang, M. (2006). Designing a Decompositional Rule Extraction Algorithm for Neural Networks. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_194
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DOI: https://doi.org/10.1007/11759966_194
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