Abstract
Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system, CN2, designed for the efficient induction of simple, comprehensible production rules in domains where problems of poor description language and/or noise may be present. Implementations of the CN2, ID3, and AQ algorithms are compared on three medical classification tasks.
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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). Bled, Yugoslavia: Sigma Press.
Chan, P. K. (1988). A critical review of CN2:A polythetic classifier system(Technical Report CS–88–09). Nashville, TN: Vanderbilt University, Department of Computer Science.
Iba, W., Wogulis, J., & Langley, P. (1988). Trading off simplicity and coverage in incremental concept learning. Proceedings of the Fifth International Conference on Machine Learning(pp. 73–79). Ann Arbor, MI: Morgan Kaufmann.
Jackson, J. (1985). Economics of automatic generation of rules from examples in a chess end-game(Technical Report UIUCDCS-F 85–932). Urbana: University of Illinois, Computer Science Department.
Kalbfleish, J. (1979). Probability and statistical inference(Vol. 2). New York: Springer-Verlag.
Kononenko, I., Bratko, I., & Roskar, E. (1984). Experiments in automatic learning of medical diagnostic rules(Technical Report). Ljubljana, Yu-goslavia: E. Kardelj University, Faculty of Electrical Engineering.
Michalski, R. S. (1969). On the quasi-minimal solution of the general covering problem. Proceedings of the Fifth International Symposium on Informa-tion Processing(pp. 125–128). Bled, Yugoslavia.
Michalski, R. S., & Chilausky, R. (1980). Learning by being told and learn-ing from examples:An experimental comparison of the two methods of knowledge acquisition in the context of developing an expert system for soybean disease diagnosis. International Journal of Policy Analysis and Information Systems, 4125–160.
Michalski, R. S., & Larson, J. (1983). Incremental generation of VL1 hypothe-ses:The underlying methodology and the description of the programAQl 1 (Technical Report ISG 83–5). Urbana: University of Illinois, Computer Science Department.
Michalski, R. S., Mozetic, I., Hong, J., & Lavrac, N. (1986). The multi-purpose incremental learning system AQl5 and its testing application to three medical domains. Proceedings of the Fifth National Conference on Artificial Intelligence(pp. 1041–1045). Philadelphia: Morgan Kaufmann.
Mowforth, P. (1986). Some applications with inductive expert system shells(TIOP 86–002). Glasgow, Scotland: Turing Institute.
Niblett, T. (1987). Constructing decision trees in noisy domains. Proceedings of the Second European Working Session on Learning(pp. 67–78). Bled, Yugoslavia: Sigma Press.
Niblett, T., & Bratko, I. (1987). Learning decision rules in noisy domains. In M. A. Bramer (Ed. ), Research and development in expert systems(Vol. 3). Cambridge: Cambridge University Press.
O'Rorke, P. (1982). A comparative study of inductive learning systemsAQ11P and ID3 using a chess end-game test problem(Technical Report ISG 82–2). Urbana: University of Illinois, Computer Science Department.
Paterson, A., & Niblett, T. (1982). ACLS manual, Version 1(Technical Report). Glasgow, Scotland: Intelligent Terminals Limited.
Quinlan, J. R. (1983). Learning efficient classification procedures and their ap-plication to chess end games. In R. S. Michalski, J. G. Carbonell, & T. M. Mitchell (Eds.), Machine learning:An artificial intelligence approach. Los Altos, CA: Morgan Kaufmann.
Quinlan, J. R. (1987a). Simplifying decision trees. International Journal of Man-Machine Studies, 27221–234.
Quinlan, J. R. (1987b). Generating production rules from decision trees. Pro-ceedings of the Tenth International Joint Conference on Artificial Intelli-gence(pp. 304–307). Milan, Italy: Morgan Kaufmann.
Quinlan, J. R., Compton, P. J., Horn, K. A., & Lazarus, L. (1987). Inductive knowledge acquisition:A case study. Applications of expert systems. Wokingham, England: Addison-Wesley.
Rivest, R. L. (1987). Learning decision lists. Machine Learning, 2229–246.
Wald, A. (1947). Sequential analysis. New York: Wiley.
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Clark, P., Niblett, T. The CN2 Induction Algorithm. Machine Learning 3, 261–283 (1989). https://doi.org/10.1023/A:1022641700528
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DOI: https://doi.org/10.1023/A:1022641700528