| Summary: | 碩士 === 元智大學 === 資訊管理研究所 === 89 === In recent years, fuzzy decision tree had been widely used to extracting classification knowledge from a set of feature-based data. And many researchers are engaged in the more efficient and optimal algorithms to construct fuzzy decision trees. However, very few papers discuss the process of defuzzification in fuzzy decision tree. Therefore, we propose a new method that emphasizes on the defuzzification process. The tree build by our method is called weighted fuzzy decision tree. It uses the concept of weighted fuzzy production rule(WFPR) in defuzzification process and the concept of fuzzy Bayesian inference(FBI) method to find the parameters needed in the inference process of WFPR. To verify the accuracy of our method for classification, standard benchmark datasets are used. When the tree is build as non-perfect decision tree, our proposed method has higher accuracy for classification than other defuzzification methods; when the tree is perfect decision tree, our method is also acceptable.
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