臺灣博碩士論文加值系統

癌症高居國人十大死因之首，由於癌症初期病患接受適時治療的存活率較高，因此若能「早期發現，早期診斷，早期治療」則可降低死亡率。本研究主要針對「表面強化雷射解析電離飛行質譜技術」(Surface-Enhanced Laser Desorption / Ionization Time-of-Flight Mass Spectrometry，SELDI-TOF-MS)所蒐集而來的攝護腺癌症蛋白質質譜之事前處理資料進行分析。目的是希望藉由Meta-Learning的方式結合分類器，並以逐步特徵選取之，期望以較少且具代表的特徵變數將資料分類，以達到較高的正確率。本文利用正確率決定逐步特徵選取時變數加入的順序，並進一步以Elastic Net與判定係數作為特徵變數排序依據，以改善變數間共線性高的問題。並且考慮投票法(多數表決法與權重投票法)以及串聯法(cascading)：多個分類器串聯與單一分類器串聯。研究發現，以判定係數刪選特徵變數加入的先後順序並以支持向量機(Support Vector Machine，SVM)串聯的特徵選取結果在各分類下皆有良好表現，為較佳的特徵選取方式。

關鍵字：特徵選取、串聯法、蛋白質質譜、meta-learning、支持向量機

第壹章 緒論 4
第一節 研究背景 4
第二節 研究動機與目的 6
第三節 研究架構 6
第貳章 蛋白質質譜資料 8
第一節 表面強化雷射解析電離飛行質譜技術 8
第二節 攝護腺癌症蛋白質質譜資料 9
第三節 蛋白質質譜資料之探討 11
第參章 文獻探討 12
第肆章 研究方法 15
第一節 分類器的介紹 16
4.1.1 LDA 16
4.1.2 KNN 18
4.1.3 SVM 21
第二節 結合多個分類器之特徵選取 25
4.2.1 Stacking 26
4.2.2 Cascading 28
第三節 特徵選取 30
第伍章 實證分析 31
第一節 投票法 33
5.1.1 多數表決法 33
5.1.2 權重投票法 36
第二節 CASCADING 37
5.2.1 多個分類器之串聯 38
5.2.2 單一分類器之串聯 42
第三節 特徵選取之改良 45
5.3.1 Elastic Net + 單一分類器之串聯 46
5.3.3 判定係數粹取法 49
第陸章 結論與建議 52
參考文獻 54
附 錄 59

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