Simple fuzzy logic rules based on fuzzy decision tree for classification and prediction problem

Abstract

In data mining for knowledge explanation purposes, we would like to build simple transparent fuzzy models. Compared to other fuzzy models, simple fuzzy logic rules (IF... THEN... rules) based on triangular or trapezoidal shape fuzzy sets are much simpler and easier to understand. For fuzzy rule based learning algorithms, choosing the right combination of attributes and fuzzy sets which have the most information is the key point to obtain good accuracy. On the other hand, the fuzzy ID3 algorithm gives an efficient model to select the right combinations. We therefore discover the set of simple fuzzy logic rules from a fuzzy decision tree based on the same simple shaped fuzzy partition, after dropping those rules whose credibility is less than a reasonable threshold, only if the accuracy of the training set using these rules is reasonably close to the accuracy using fuzzy decision tree. The set of simple fuzzy logic rules satisfied with this condition is also able to be used to interpret the information of the tree. Furthermore, we use the fuzzy set operator "OR" to merge simple fuzzy logic rules to reduce the number of rules. © 2005 by International Federation for Information Processing.