Weighted Fuzzy Production Rule And Its Reasoning In Decision Tree

Decision tree learning is one of the most widely used and practical methods for inductive reasoning. Learning trees can also be re-represented as sets of if-then rules to improve human readability. The basis of finding useful knowledge is a good reasoning accuracy of tree. How to improve reasoning accuracy of tree is one of directs to study in decision tree. In this article, we compare two inductive, i.e. FuzzyID3 and Yuan and Shaw algorithms. We find Fuzzy ID3 algorithm is better than Yuanand Shaw algorithm in training accuracy, testing accuracy by experimental andtheoretical analysis. This paper proposes a new approach to refine the fuzzy production rules, which assigns local weights to proposition of fuzzy production rules by using a linear programming procedure. In addition to the reasoning accuracy improvement, this approach has a number of advantages such as intuitive background of local weights, non-increasing of number of rules, and less computational effort for obtaining local weights.