A New Heuristic Measure for Learning Rules from Fuzzy Data

Abstract

Decision tree induction and rule production methods have been proven as efficient tools in concept learning or data mining tasks. These approaches exhibit a good performance even when there are cognitive uncertainties in the data. Most systems in this paradigm use the information gain criterion in selecting attributes when learning. This paper presents an alternative approach. A heuristic measure of the impurity level of rules when dealing with fuzzy data is described and used in a classification algorithm. Results on the Sports classification problem are reported and compared with those of other learning algorithms.