Abstract
A knowledge representation based on the probability theory is currently the most popular way of handling uncertainty. However, rule based systems are still popular. Their advantage is that rules are usually more easy to interpret than probabilistic models. A conversion method would allow to exploit advantages of both techniques. In this paper an algorithm that converts Naive Bayes models into rule sets is proposed. Preliminary experimental results show that rules generated from Naive Bayes models are compact and accuracy of such rule-based classifiers are relatively high.
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References
1. E.G. Buchanan and H. Shortliffe. Rule-based expert systems: The MYCIN experiments of the Stanford heuristic programming project. Addison-Wesley, 1984.
2. C.L. Blake D.J. Newman, S. Hettich and C.J. Merz. UCI repository of machine learning databases, 1998.
3. M.J. Druzdzel. A development environment for graphical decision-analytic models. In Proc. of the 1999 Annual Symposium of the American Medical Informatics Association (AMIA-1999), page 1206, Washington, B.C., 1999.
4. N. Friedman, D. Geiger, and M. Goldszmidt. Bayesian network classifiers. Machine Learning, 29(2–3):131–163, 1997.
5. D. Heckerman. Probabilistic interpretation for MYCIN's uncertainty factors, pages 167–196. North-Holland, 1986.
6. D.E. Heckerman. An empirical comparison of three inference methods. In Proceedings of the Fourth Workshop on Uncertainty in Artificial Intelligence, pages 158–169. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, 1988.
7. D. Roller and A. Pfeffer. Probabilistic frame-based systems. In Proc. of 15th National Conference on Artificial Intelligence AAAI-98, pages 580–587, 1998.
8. M. Korver and P. Lucas. Converting a rule-based expert system into a belief network. Medical Informatics, 18(3):219–241, 1993.
9. P.J.F. Lucas. Certainty-factor-like structures in bayesian belief networks. Knowl.-Based Syst, 14(7):327–335, 2001.
10. P.J.F. Lucas and A.R. Janssens. Development and validation of hepar, an expert system for the diagnosis of disorders of the liver and biliary tract. Medical Informatics, 16:259–270, 1991.
11. R. S. Michalski and I. Imam. Learning problem-oriented decision structures from decision rules: The aqdt-2 system. In Methodology for Intelligent Systems of the 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94), volume 869 of Lecture Notes in Artificial Intelligence, pages 416–426. Springer, 1994.
12. B. Middleton, M. Shwe, Heckerman, M. Henrion D. E., E. J. Horvitz, H. Lehmann, and G. F. Cooper. Probabilistic diagnosis using a reformulation of the internist-1/qmr knowledge base ii: Evaluation of diagnostic performance. Methods of Information in Medicine, 30:256–267, 1991.
13. A. Newell and H.A. Simon. Human Problem Solving. Prentice-Hall, 1972.
14. A. Onisko, P. Lucas, and M.J. Druzdzel. Comparison of rule-based and Bayesian network approaches in medical diagnostic systems. Lecture Notes in Computer Science, 2101:283+, 2001.
15. D. Poole. Probabilistic horn abduction and bayesian networks. Artificial Intelligence, 64(1):81–129, 1993.
16. J.R. Quinlan. C4-5: Programs for Machine Learning. Morgan Kaufmann, 1993.
17. M. Shwe, B. Middleton, D. E. Heckerman, M. Henrion, E. J. Horvitz, H. Lehmann, and G. F. Cooper. Probabilistic diagnosis using a reformulation of the internist-1/qmr knowledge base i: Probabilistic model and inference algorithms. Methods of Information in Medicine, 30:241–255, 1991.
18. B. Sniezynski. Choice of a knowledge representation method for learning classifiers in medical domains. Journal of Medical Informatics and Technologies, 6, 2005.
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Śnieżyński, B. (2006). Converting a Naive Bayes Model into a Set of Rules. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33521-8_22
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DOI: https://doi.org/10.1007/3-540-33521-8_22
Publisher Name: Springer, Berlin, Heidelberg
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