模糊決策樹之解模糊化

The Defuzzification for Fuzzy Decision Tree

奚世峰(Shi-Feng Hsi)

指導教授 ： 詹前隆

元智大學/資訊學院/資訊管理學系/碩士(2001年)

摘要

最近幾年，模糊決策樹(fuzzy decision tree)已被廣泛應用在萃取資料規則及分類上，透過此類的演算法，找出可能有問題的資料(如盜刷信用卡、醫療費用浮報等)及預測新事物的類別。而很多學者都在致力於建構最佳化的模糊決策樹演算法，以提高預測的準確性。這些研究主要都是在探討決策樹的建置過程，包括了在切割連續屬性時歸屬函數的決定，或是建構模糊決策樹時所採用的模糊熵(fuzzy entropy)演算法等，而卻很少在探討建置模糊決策樹後，在推論過程所必經的步驟─解模糊化(defuzzification)。因此本研究著重在提出新的解模糊化方法，運用了加權模糊推論規則的概念在解模糊化上，所建構出的決策樹稱為加權模糊決策樹(weighted fuzzy decision tree)。
此加權模糊決策樹結合了加權模糊推論規則(weighted fuzzy production rule)及模糊貝氏推論法(fuzzy Bayesian inference)。在解模糊化的過程中主要是運用了加權模糊推論規則的概念來完成，而其中所需的參數─權重，則是利用模糊貝氏推論法來取得。此加權模糊決策樹將落實資料探勘的目的，從一堆資料中，找出規則，進而預測及分類新資料，進而產生新知識。在驗證方面，採用標準測試資料實證此方法的正確性，當樹建構成非完全決策樹時，我們的方法較其他解模糊化方法(x-x-+及KNN)分類的正確性來的佳；當樹建構成完全決策樹時，也有不錯的成效。

關鍵字

資料探勘 ； 加權模糊決策樹 ； 解模糊化 ； 知識取得

並列摘要

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.

並列關鍵字

data mining ； weighted fuzzy decision tree ； defuzzification ； knowledge acquisition

參考文獻

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