在本研究中，我們進一步改良了基因的選取方法，結合7個基因的挑選方法，稱為整體學習演算法(Ensemble Voting)。基因表現資料包含有白血病、前列腺癌和乳癌的資料。從三種微陣列表現資料中，我們發現由七個基礎學習演算法，針對每次產生的結果進行投票的Ensemble Voting得到的結果比單一種特徵選取方法的準確度還高，而在每一種基因表現資料集合或者是不同的基因數量都有著相當不錯的結果，不會因為基因表現資料集合或者基因數目的不同，準確度就大大的降低，都會維持在水準以上，是一個較適合針對基因表現資料進行處理的分析技術。
利用Ensemble Voting的方式整合生物統計分析和機器學習的七種特徵選取方法，是一個強而有力的有效方法，並且能提高分類性能，得到比原先方法更高的準確性。

In this paper, we describe a framework for selecting informative genes, called Ensemble Voting method, which combines seven gene selection methods. We conducted experiments using leukemia, prostate cancer, and breast cancer data sets. The results show that the ensemble voting framework is a robust and efficient approach to identify informative genes and to improve classification performance from microarray data. The ensemble voting approach had an equal or better performance than each individual feature selection method almost in all range of genes selected. The results indicate that ensemble voting method might be a viable and feasible approach for the microarray gene expression analysis.

第一章、序論 - 1 -
1.1 研究背景 - 1 -
1.3 研究目的 - 3 -
第二章、文獻探討 - 5 -
2.1 分類問題 - 10 -
2.2 特徵選取 - 11 -
第三章、研究方法 - 14 -
3.1生物統計分析特徵選取方法 - 15 -
3.1.1 t-Test - 15 -
3.1.2 Fisher - 17 -
3.1.3 Golub - 19 -
3.1.4 TNoM - 20 -
3.1.5 MDMR - 22 -
3.1.6 WEPO - 23 -
3.2 機器學習特徵選取方法 RANDOM FORESTS - 26 -
3.3 ENSEMBLE VOTING - 29 -
3.4 分類方法 - 32 -
3.5 尋找具有生物意義的重要基因 - 34 -
第四章、系統建構 - 35 -
4.1 資料庫與資料結構設計 - 36 -
4.2 系統架構設計與流程圖 - 37 -
4.3 系統介面 - 39 -
4.4 使用說明 - 40 -
第五章、結果與討論 - 42 -
5.1微陣列基因表現資料 - 42 -
5.2七種特徵選取方法研究結果與比較分析 - 43 -
5.2.1分類準確度 - 43 -
5.2.2探討生物意義的重要基因 - 50 -
5.3 ENSEMBLE VOTING研究結果與比較分析 - 52 -
第六章、結論與未來展望 - 57 -
6.1 結論 - 57 -
6.2 未來展望 - 59 -
參考文獻 - 60 -

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