臺灣博碩士論文加值系統

本研究針對多類別的大腸桿菌資料庫利用特徵選取的方式來縮減變數。建構三種混合式分類器分別為決策樹結合類神經網路、支援向量機混合決策樹、貝氏網路混合決策樹。特徵選取中，本研究以Information Gain萃取重要的特徵變數。實驗結果可知，本研究所建構三種混合式分類器準確率都有不錯表現，最高準確率為94.05%。為了驗證本研究方法的適用性，不侷限於某一特定領域，再利用帕金森氏症資料庫來驗證其最高準確率為95.38%。

This paper utilizes Information Gain to extract important features, and compares the performances among combined decision tree (C5) and artificial neural network(ANN)、hybrid support vector machine(SVM) and C5、hybrid bayesian network(BN) and C5. Three hybrid classifiers are developed in this study and the highest accuracy is 94.05% in the multiple–class E.coli dataset. In order to explain this research is widely applicable and is not limited to particular field, we again verify this research architecture on Parkinson dataset and the accuracy is up to 95.38%.

摘要 I
Abstract II
致謝 III
表目錄 VI
圖目錄 VII
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 1
1.3 研究範圍與對象 2
1.4 研究流程 2
1.5 研究架構 3
第二章 文獻探討 4
2.1 資料探勘 4
2.2 決策樹 5
2.3 Information Gain 6
2.4 類神經網路 7
2.5 倒傳遞類神經網路 8
2.6 交叉驗證 10
2.7 支援向量機 10
2.7.1 線性支援向量機 10
2.7.2 非線性支援向量機 13
2.8 貝氏網路 14
2.9 混合式分類器相關文獻 15
第三章 研究方法 17
3.1 資料說明 18
3.2 資料前處理 20
3.3 倒傳遞類神經網之建構 21
3.4 建構C5決策樹結合ANN倒傳遞類神經網路分類器 22
3.5 建構 SVM支援向量機混合C5決策樹分類器 24
3.6 建構BN貝氏網路混合C5決策樹分類器 25
3.7 田口品質工程之參數設計 26
3.7.1 大腸桿菌資料庫SVM-參數設計 26
3.7.2 帕金森氏症資料庫SVM-參數設計 29
第四章 研究結果與討論 32
4.1 基本統計分析 32
4.2 Information Gain於大腸桿菌與帕金森氏症資料庫特徵選取能力的比較 35
4.3 混合式分類器於大腸桿菌及帕金森氏症資料庫準確率 35
第五章 結論與未來研究方向 40
參考文獻 41

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