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系統識別號 U0026-0812200912014858
論文名稱(中文) 以基因演算法調整模糊熵建立模糊決策樹之研究
論文名稱(英文) A fuzzy decision tree with fuzzy entropy turned by genetic algorithm
校院名稱 成功大學
系所名稱(中) 資訊管理研究所
系所名稱(英) Institute of Information Management
學年度 94
學期 2
出版年 95
研究生(中文) 王元璋
研究生(英文) Yuang-Jang Wang
學號 r7691110
學位類別 碩士
語文別 中文
論文頁數 79頁
口試委員 口試委員-利德江
指導教授-吳植森
口試委員-蔡長鈞
中文關鍵字 基因演算法  模糊決策樹  模糊熵 
英文關鍵字 Genetic Algorithm  Fuzzy Entropy  Fuzzy Decision Tree 
學科別分類
中文摘要   決策樹為處理資料探勘問題的方法論其中之一,它可以透過學習,產生規則並做為決策支援系統或高階主管資訊系統之後端背景資訊,以提供決策時之參考,故而是一種受歡迎的資料探勘方法。而模糊決策樹具有語意與歸屬程度的表達方式,其中模糊歸屬函數是影響其正確性的主要因素之一。
  本研究建立兩種分類模型,第一種使用基因演算法為基礎之分類模型,其使用實數型基因演算法,對於各分類找出較佳的分類基因。第二種分類模型運用Quinlan熵之決策樹與Janikow的模糊熵演算法為基礎,搜尋多組模糊決策樹之歸屬函數,並使用第一種基因分類模型,找尋分類準確率較佳之模糊歸屬函數。在模糊歸屬函數中,根據分類別之不同使用兩個語意變數與三個語意變數形成模糊決策樹。
  本研究以UCI-ML為研究學習與測試資料庫,經由數值範例實驗研究顯示,基因演算法與模糊決策樹比naïve bayesian與C4.5有較佳的分類準確率。並對於臨界值與不確定屬性邊界,管理決策者並可在領域知識的範疇中,調整模糊歸屬函數以達最佳的分類準確率。

英文摘要   The decision tree is one of data mining methodologies. It generates rules via learning to provide back-end information to decision support systems (DSS) or executive information system (EIS) for decision making. Regular decision trees lack of ability to deal with uncertainty. Fuzzy decision trees handle uncertainty using linguistic variable with adjustable membership functions. The membership function of fuzzy decision tree can adapt itself to various situation for gaining decision accuracy.

  This study builds two kinds of classification model. The first type is based on genetic algorithms. It uses a real number type genetic algorithm and a fitness function, which is easy to evaluate. The second type uses fuzzy decision tree model based on fuzzy entropy of Janikow and entropy of Quinlan to perform algorithm. This model searches the best membership function of fuzzy decision trees using genetic algorithm. In the study, three linguistics and two linguistics are used to form the fuzzy decision tree depending on the problems encountered.

  UCI-ML is used as the research database. The study shows that both of the genetic algorithm and fuzzy decision tree have better rates of accuracy than those of Naïve Bayesian and C4.5 Based decision tree. The advantage of the proposed fuzzy decision tree is that the decision boundary can be adjusted to improve accuracy using domain knowledge of managers.

論文目次 第一章 緒論                           1
    第一節 研究動機                     1
    第二節 研究目的                     2
    第三節 研究流程                     5
    第四節 研究範圍與限制                  5
    第五節 論文架構                     7
第二章 文獻探討                         8
    第一節 模糊理論                     8
        2.1.1 模糊集合              8
        2.1.2 模糊理論基本運算          10
        2.1.3 語意變數              11
    第二節 基因演算法                   12
        2.2.1 基因演算法之基本觀念        12
        2.2.2 基因演算法的基本流程        17
        2.2.3 基因演算法的特色          18
        2.2.4 實數型基因演算法          20
    第三節 決策樹                     22
    第四節 模糊決策樹                   26
    第五節 運用模糊熵建構模糊決策樹架構          32
第三章 研究方法                        40
    第一節 建立研究流程架構                40
    第二節 基因演算法之分類模型              40
    第三節 以基因演算法搜尋模糊歸屬函數之模糊決策樹方法  47
第四章 數值實驗分析                      56
    第一節 基因演算法分類模型之數值範例分析        56
        4.1.1 鳶尾花資料庫之分類分析       56
        4.1.2 隱形眼鏡資料庫之分類分析      58
        4.1.3 心理學資料庫之分類分析       59
    第二節 以基因演算法搜尋歸屬函數之模糊決策樹分析    61
        4.2.1 鳶尾花資料庫之分類分析       61
        4.2.2 隱形眼鏡資料庫之分類分析      62
        4.2.3 心理學資料庫之分類分析       64
    第三節 簡易貝氏分類數值範例分析            65
    第四節 決策樹分類數值範例分析             67
    第五節 各種分類之正確率效能比較            71
第五章 結論與建議                       74
參考文獻                            76
參考文獻 參考文獻
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