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 for handling continuous attributes and noisy data are needed. This paper presents a new rule induction algorithm called RULES-6, derived from the RULES-3 Plus algorithm. The algorithm employs a fast and noise-tolerant search method for

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... racting IF-THEN rules from examples. It also uses simple and effective methods for rule evaluation and continuous attributes handling. A detailed empirical evaluation of the algorithm is reported in the paper. The results presented demonstrate the strong performance of the algorithm. _____________________________________________________________ 1 Instances of the target class (the class of the training instance under consideration) in the training set are called positive instances. Instances in the training set that do not belong to the target class are called negative instances.