Abstract
Automated knowledge acquisition is an important research issue to solve the bottleneck problem in developing expert systems. There have been proposed several methods of inductive learning, such as induction of decision trees, AQ method, and neural networks for this purpose. However, most of the approaches focus on inducing some rules which classify cases correctly. On the contrary, medical experts also learn other information which is important for medical diagnostic procedures from databases. In this paper, a ruleinduction system, called PRIMEROSEREX (Probabilistic Rule Induction Method based on Rough Sets and Resampling methods for Expert systems), is introduced. This program extracts not only classification rules for differential diagnosis, but also other medical knowledge needed for other diagnostic procedures, based on a diagnosing model of a medical expert system RHINOS (Rule-based Headache and facial pain INformation Orgranizing System). This system is evaluated by using training samples of RHINOS domain, and the induced results are compared with rules acquired from medical experts. The results show that our proposed method correctly induces RHINOS rules and estimate the statistical measures of rules.
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References
Breiman, L., Freidman, J., Olshen, R., and Stone, C. (1984). Classification And Regression Trees. Belmont, CA: Wadsworth International Group.
Buchnan, B. G. and Shortliffe, E. H.(eds.) (1984). Rule-Based Expert Systems, Addison-Wesley.
Cestnik, B., Kononenko, I., Bratko, I. (1987). Assistant 86: A knowledge elicitation tool for sophisticated users. Proceedings of the Second European Working Session on Learning, pp.31–45, Sigma Press.
Clark, P., Niblett, T. (1989). The CN2 Induction Algorithm. Machine Learning, 3,261–283.
Efron, B. (1982). The Jackknife, the Bootstrap and Other Resampling Plans. Society for Industrial and Applied Mathematics, Pennsylvania.
Efron, B. (1983). Estimating the error rate of a prediction rule: improvement on cross validation. Journal of American Statistics Association, 78, 316–331.
Indurkhya, N. and Weiss, S. (1991). Iterative Rule Induction Methods, Applied Intelligence, 1, 43–54.
Matsumura, Y., et al. (1986). Consultation system for diagnoses of headache and facial pain: RHINOS. Medical Informatics, 11, 145–157.
Mclachlan, G. J. (1992). Discriminant Analysis and Statistical Pattern Recognition. John Wiley and Sons, New York.
Michalski, R. S. (1983). A Theory and Methodology of Machine Learning. Michalski, R.S., Carbonell, J.G. and Mitchell, T.M., Machine Learning — An Artificial Intelligence Approach. Morgan Kaufmann, Palo Alto, CA.
Michalski, R. S., Mozetic, I., Hong, J., and Lavrac, N. (1986). The Multi-Purpose Incremental Learning System AQ15 and its Testing Application to Three Medical Domains. Proceedings of the fifth National Conference on Artificial Intelligence, 1041–1045, AAAI Press, Palo Alto, CA.
Pawlak, Z. (1991). Rough Sets. Kluwer Academic Publishers, Dordrecht.
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81–106.
Quinlan, J.R. (1993). C4.5 — Programs for Machine Learning, Morgan Kaufmann, CA.
Tsumoto, S. and Tanaka, H. Induction of Medical Expert System Rules based on Rough Sets and Resampling Methods Proceedings of the Eighteenth Annual Symposium on Computer Applications in Medical Care, (Journal of the AMIA 1, supplement), pp.1066–1070, 1994.
Walker, M. G. and Olshen, R. A. (1992). Probability Estimation for Biomedical Classification Problems. Proceedings of the sixteenth Symposium on Computer Applications on Medical Care, McGrawHill, New York.
Ziarko, W. (1991). The Discovery, Analysis, and Representation of Data Dependencies in Databases, in: Shapiro, G. P. and Frawley, W. J.(eds), Knowledge Discovery in Databases, AAAI press, Palo Alto, CA, pp.195–209.
Ziarko, W. (1993). Variable Precision Rough Set Model. Journal of Computer and System Sciences, 46, 39–59.
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© 1996 Springer-Verlag Berlin Heidelberg
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Tsumoto, S., Tanaka, H. (1996). Induction of expert system rules from databases based on rough set theory and Resampling methods. In: RaÅ›, Z.W., Michalewicz, M. (eds) Foundations of Intelligent Systems. ISMIS 1996. Lecture Notes in Computer Science, vol 1079. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61286-6_138
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DOI: https://doi.org/10.1007/3-540-61286-6_138
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