Abstract:
This paper describes a family of rule generators that can be used to extract classification rules in various applications. It includes versions that can handle noise in data, that can produce perfect rules, that can induce order independent or dependent rules based on a core algorithm. Evaluation measures are given to compare different rule generators in the family and with some known methods. Empirical results and applications of these rule generators in the family show that they are efficient, compact and accurate in various situations. The systems are available for trial use upon request.
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