Three discretization methods for rule induction

We discuss problems associated with induction of decision rules from data with numerical attributes. Real-life data frequently contain numerical attributes. Rule induction from numerical data requires an additional step called discretization. In this step numerical values are converted into intervals. Most existing discretization methods are used before rule induction, as a part of data preprocessing. Some methods discretize numerical attributes while learning decision rules. We compare the classification accuracy of a discretization method based on conditional entropy, applied before rule induction, with two newly proposed methods, incorporated directly into the rule induction algorithm LEM2, where discretization and rule induction are performed at the same time. In all three approaches the same system is used for classification of new, unseen data. As a result, we conclude that an error rate for all three methods does not show significant difference, however, rules induced by the two new methods are simpler and stronger. (C) 2001 John Wiley & Sons, Inc.