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Induction of ripple-down rules applied to modeling large databases

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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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Authors and Affiliations

  1. Knowledge Science Institute, University of Calgary, T2N 1N4, Calgary, Alberta, Canada

    B. R. Gaines

  2. Department of Computer Science, University of New South Wales, 2033, Sydney, Australia

    P. Compton

Authors
  1. B. R. Gaines
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  2. P. Compton
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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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