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Issue No.05 - September-October (1997 vol.9)
pp: 709-717
ABSTRACT
<p><b>Abstract</b>—Generating classification rules or decision trees from examples has been a subject of intense study in the pattern recognition community, the statistics community, and the machine-learning community of the artificial intelligence area. We pursue a point of view that minimality of rules is important, perhaps above all other considerations (biases) that come into play in generating rules. We present a new minimal rule-generation algorithm called R-MINI (Rule-MINI) that is an adaptation of a well-established heuristic-switching-function-minimization technique, MINI. The main mechanism that reduces the number of rules is repeated application of generalization and specialization operations to the rule set while maintaining completeness and consistency. R-MINI results on some benchmark cases are also presented.</p>
INDEX TERMS
Classification, minimal rules, DNF, Occam's razor, generalization, specialization.
CITATION
Se June Hong, "R-MINI: An Iterative Approach for Generating Minimal Rules from Examples", IEEE Transactions on Knowledge & Data Engineering, vol.9, no. 5, pp. 709-717, September-October 1997, doi:10.1109/69.634750
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