Abstract
A methodology forthe modeling of large data sets is described which results in rule sets having minimal inter-rule interactions, and being simply maintained. An algorithm for developing such rule sets automatically is described and its efficacy shown with standard test data sets. Comparative studies of manual and automatic modeling of a data set of some nine thousand five hundred cases are reported. A study is reported in which ten years of patient data have been modeled on a month by month basis to determine how well a diagnostic system developed by automated induction would have performed had it been in use throughout the project.
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References
Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984).Classification and Regression Trees. Belmont, Wadsworth.
Cendrowska, J. (1987). An algorithm for inducing modular rules.International Journal of Man-Machine Studies, 27(4), 349–370.
Compton, P., Edwards, G., Kang, B., Malor, R., Menzies, T., Preston, P., Srinivasan, A., and Sammut, S. (1991). Ripple down rules: possibilities and limitations. J.H. Boose and B.R. Gaines (Eds.),Proceedings of the Sixth AAAI Knowledge Acquisition for Knowledge-Based Systems Workshop (pp. 6-1–6-20). Calgary, Canada, University of Calgary.
Compton, P. and Jansen, R. (1990a). Knowledge in context: a strategy for expert system maintenance. C.J. Barter and M.J. Brooks (Eds.),AI'88: 2nd Australian Joint Artificial Intelligence Conference, Adelaide Australia, November 1988, Proceedings (pp. 292–306). Berlin, Springer.
Compton, P. and Jansen, R. (1990b). A philosophical basis for knowledge acquisition.Knowledge Acquisition, 2(3), 241–258.
Gaines, B.R. (1977). System identification, approximation and complexity.International Journal of General Systems, 2(3), 241–258.
Gaines, B.R. (1989). An ounce of knowledge is worth a ton of data: quantitative studies of the trade-off between expertise and data based on statistically well-founded empirical induction.Proceedings of the Sixth International Workshop on Machine Learning (pp. 156–159). San Mateo, California, Morgan Kaufmann.
Gaines, B.R. (1991a). Empirical investigations of knowledge representation servers: Design issues and applications experience with KRS.ACM SIGART Bulletin, 2(3), 45–56.
Gaines, B.R. (1991b). The tradeoff between knowledge and data in data acquisition. G. Piatetsky-Shapiro and W. Frawley (Ed.),Knowledge Discovery in Databases (pp. 491–505). Cambridge, Massachusetts, AAAI/MIT Press.
Gaines, B.R. (1994). Class library implementation of an open architecture knowledge support system.International Journal Human-Computer Studies, 41(1/2), 59–107.
Horn, P.J., Compton, P.J., Lazarus, L., and Quinlan, J.R. (1985). An expert system for the interpretation of thyroid assays in a clinical laboratory.Australian Computer Journal, 17, 7–11.
Li, X. (1991). What's so bad about rule-based programming?IEEE Software, 8(5), 103–105.
Mansuri, Y., Kim, J.G., Compton, P., and Sammut, C. (1991). A comparison of a manual knowledge acquisition method and an inductive learning method.Proceedings of the First Australian Workshop on Knowledge Acquisition for Knowledge-Based Systems (pp. 114–132). Sydney, University of Sydney.
Piatetsky-Shapiro, G. and Frawley, W. (Ed.) (1991).Knowledge Discovery in Databases. Cambridge, Massachusetts, MIT Press.
Press, W.H., Flannery, B.P., Teukolsky, S.A., and Vetterling, W.T. (1988).Numerical Recipes in C: The Art of Scientific Computing. Cambridge, UK, Cambridge University Press.
Quinlan, J.R. (1979). Discovering rules by induction from large collections of examples. D. Michie (Ed.),Expert Systems in the Micro Electronic Age (pp. 168–201). Edinburgh, Edinburgh University Press.
Quinlan, J.R. (1987). Simplifying decision trees.International Journal of Man-Machine Studies, 27(3), 221–234.
Quinlan, J.R. (Ed.) (1993).C4.5: Programs for Machine Learning. San Mateo, California Morgan-Kaufman.
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Gaines, B.R., Compton, P. Induction of ripple-down rules applied to modeling large databases. J Intell Inf Syst 5, 211–228 (1995). https://doi.org/10.1007/BF00962234
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DOI: https://doi.org/10.1007/BF00962234