Abstract
In this paper, fuzziness existing in the process of generating decision trees by discretizing continuous-valued attributes is considered. In a sense a better way to express this fuzziness via fuzzy numbers is presented using possibility theory. The fact that selection of membership 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 revised algorithm leads to more reasonable and more natural decision trees.
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