ON THE HANDLING OF FUZZINESS FOR CONTINUOUS-VALUED ATTRIBUTES IN DECISION TREE GENERATION

In this paper, fuzziness existing in the process of generating decisio n trees by discretizing continuous-valued attributes is considered. In a sense a better way to express this fuzziness via fuzzy numbers is p resented using possibility theory. The fact that selection of membersh ip functions in a class of symmetric distributions does not influence the decision tree generation is proved. The validity of using the tree to classify future examples is explained. On the basis of likelihood possibility maximization, the existing algorithm is revised. The revis ed algorithm leads to more reasonable and more natural decision trees. (C) 1998 Published by Elsevier Science B.V. All rights reserved.