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Quo Vadis? Reliable and Practical Rule Extraction from Neural Networks

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Advances in Machine Learning I

Part of the book series: Studies in Computational Intelligence ((SCI,volume 262))

Abstract

Rule extraction from neural network algorithms have been investigated for two decades and there have been significant applications. Despite this level of success, rule extraction from neural network methods are generally not part of data mining tools, and a significant commercial breakthrough may still be some time away. This paper briefly reviews the state-of-the-art and points to some of the obstacles, namely a lack of evaluation techniques in experiments and larger benchmark data sets. A significant new development is the view that rule extraction from neural networks is an interactive process which actively involves the user. This leads to the application of assessment and evaluation techniques from information retrieval which may lead to a range of new methods.

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References

  • Andrews, R., Diederich, J., Tickle, A.B.: A Survey and Critique of Techniques For Extracting Rules From Trained Artificial Neural Networks. Knowledge Based Systems 8, 373–389 (1995)

    Article  Google Scholar 

  • Baesens, B., Setiono, R., Mues, C., Vanthienen, J.: Using neural network rule extraction and decision tables as management science tools for credit-risk evaluation. Management Science 49(3), 312–329 (2003)

    Article  Google Scholar 

  • Barakat, N., Diederich, J.: Eclectic rule extraction from support vector machines. International Journal of Computational Intelligence 2(1), 59–62 (2005)

    Google Scholar 

  • Caruana, R., Niculescu-Mizil, A.: An Empirical Comparison of Supervised Learning Algorithms. In: Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh (2006)

    Google Scholar 

  • Cohen, J.: The earth is round (p <.05). American Psychologist 49, 997–1003 (1994)

    Google Scholar 

  • Cawsey, A.: Explanation and Interaction. The Computer Generation of Ex-planatory Dialogues. MIT Press, Cambridge (1993)

    Google Scholar 

  • Craven, M., Shavlik, J.: Using sampling and queries to extract rules from trained neural networks. In: Proceedings of the 11th International Conference on Machine Learning, pp. 37–45 (1994)

    Google Scholar 

  • Davis, R., Buchanan, B.G., Shortliffe, E.: Production rules as a representation for a knowledge-based consultation program. Artificial Intelligence 8(1), 15–45 (1977)

    Article  MATH  Google Scholar 

  • Demsar, J.: On the Appropriateness of Statistical Tests in Machine Learning. In: The 3rd workshop on Evaluation Methods for Machine Learning In conjunc-tion with ICML 2008, Helsinki, Finland (2008)

    Google Scholar 

  • d’Avila Garcez, A.S., Broda, K., Gabbay, D.M.: Symbolic knowledge extrac-tion from trained neural networks: A sound approach. Artificial Intelligence 125, 155–207 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  • Diederich, J. (ed.): Rule Extraction from Support Vector Machines. Studies in Computational Intelligence, vol. 80. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  • Diederich, J.: Rule Extraction from Support Vector Machines – An Introduction. In: Diederich, J. (ed.) Rule-Extraction from Support Vector Machines. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  • Dietterich, T.G.: Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Computation 10(7), 1895–1923 (1998)

    Article  Google Scholar 

  • Fung, G., Sandilya, S., Rao, R.: Rule extraction for linear support vector machines. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2005)

    Google Scholar 

  • Hayward, R., Nayak, R., Diederich, J.: Using Predicates to Explain Networks. In: ECAI 2000 Workshop: Foundations of Connectionist-Symbolic Integration: Representation, Paradigms and Algorithms, Berlin, Germany (2000)

    Google Scholar 

  • Huang, J., Ling, C.X.: Using AUC and Accuracy in Evaluating Learning Algorithms. IEEE Transactions on Knowledge and Data Engineering 17(3), 299–310 (2005)

    Article  Google Scholar 

  • Huang, J., Ling, C.X.: Constructing New and Better Evaluation Measures for Machine Learning. In: The Twentieth International Joint Conference on Artificial Intelligence, pp. 859–864 (2007)

    Google Scholar 

  • KDD nuggets, http://www.kdnuggets.com/software/suites.html#N

  • Law, E.: The Problem of Accuracy as an Evaluation Criterion. In: The 3rd workshop on Evaluation Methods for Machine Learning, in conjunction with ICML 2008, Helsinki, Finland (2008)

    Google Scholar 

  • Ling, C.X., Huang, J., Zhang, H.: AUC: A Better Measure than Accuracy in Comparing Learning Algorithms. In: Proceedings 16th Conference of the Canadian Society for Computational Studies of Intelligence, AI 2003, Halifax, Canada, June 11-13. LNCS (LNAI), pp. 329–341. Springer, Heidelberg (2003)

    Google Scholar 

  • Little, L.: Reporting Results of Common Statistical Tests in APA Format (2008), http://depts.washington.edu/psywc/handouts/pdf/stats.pdf (October 11, 2008)

  • Maxwell, S., Delaney, H.: Designing experiments and analyzing data: a model comparison perspective, 2nd edn. Lawrence Erlbaum Associates Inc., New Jersey (2004)

    Google Scholar 

  • Moore, J.D.: A reactive approach to explanation in expert and advice-giving systems. PhD thesis. Los Angeles, University of California (1989)

    Google Scholar 

  • Moore, J.D., Swartout, W.R.: A Reactive Approach to Explanation. In: IJCAI 1989 International Joint Conference on Artificial Intelligence, pp. 1504–1510 (1989)

    Google Scholar 

  • Provost, F., Fawcett, T., Kohavi, R.: The Case Against Accuracy Estimation for Comparing Induction Algorithms. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 445–453 (1998)

    Google Scholar 

  • Quinlan, R.: C4.5: Programs for machine learning. Morgan Kaufman, San Mateo (1993)

    Google Scholar 

  • Setiono, R., Thong, J.Y.L.: An Approach to Generate Rules from Neural Net-works for Regression Problems (2001)

    Google Scholar 

  • Setiono, R., Leow, W.K., Zurada, J.M.: Extraction of rules from artificial neural networks for nonlinear regression (2002)

    Google Scholar 

  • Tickle, A., Andrews, R., Golea, M., Diederich, J.: The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural network. IEEE Transactions on Neural Networks 9(6), 1057–1068 (1998)

    Article  Google Scholar 

  • Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Paperback 162–164 (1999)

    Google Scholar 

  • Yamamoto, C.H., Oliveira, M.C.F., Rezende, S.O.: Including the user in the knowledge discovery look: interactive itemset-driven rule extraction. In: SAC 2008: Proceedings of the 2008 ACM symposium on Applied computing (March 2008)

    Google Scholar 

  • Zhang, Y., Su, H.-Y., Jia, T., Chu, J.: Rule extraction from trained support vector machines. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 61–70. Springer, Heidelberg (2005)

    Google Scholar 

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Diederich, J., Tickle, A.B., Geva, S. (2010). Quo Vadis? Reliable and Practical Rule Extraction from Neural Networks. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds) Advances in Machine Learning I. Studies in Computational Intelligence, vol 262. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05177-7_24

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  • DOI: https://doi.org/10.1007/978-3-642-05177-7_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05176-0

  • Online ISBN: 978-3-642-05177-7

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