RULES-3 Plus is a member of the RULES family of simple inductive learning algorithms with successful engineering applications. However, it requires modification in order to be a practical tool for problems involving large data sets. In particular, efficient mechanisms are needed for handling continuous attributes and noisy data. This article presents a new rule induction algorithm called RULES-6, which is derived from the RULES-3 Plus algorithm. The algorithm employs a fast and noise-tolerant search method for extracting IF-THEN rules from examples. It also uses simple and effective methods for rule evaluation and handling of continuous attributes. A detailed empirical evaluation of the algorithm is reported in this paper. The results presented demonstrate the strong performance of the algorithm.
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