運用集成學習分類法於白血病腫瘤基因之研究

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Please use this identifier to cite or link to this item: https://irlib.pccu.edu.tw/handle/987654321/20344

Title:	運用集成學習分類法於白血病腫瘤基因之研究
Authors:	趙李英記
Contributors:	資訊管理學系
Keywords:	K 最近鄰居分類支持向量機貝氏分類法冗餘度分析法留一交叉驗證法
Date:	2010
Issue Date:	2011-11-14 13:29:38 (UTC+8)
Abstract:	癌症已經連續多年高居國人十大死因的首位，但一般癌症發現時期均屬癌症末期，此時外科手術已經到了很難治癒的階段。早期發現輕微徵兆的癌症，傳統病理學家可以依腫瘤的形態學加以分類，但這種分類方法對於某些組織晚期其病理形態相類似，對於癌症的病程和預後已經是各自迥異，無法加以有效的區分。迄今，分子診斷所利用的基因晶片，可以對數以萬計的基因在不同組織表現去進行跟蹤監測，有助於腫瘤組織的鑑別分類和新病型的發現。本研究利用資料探勘技術來說明在腫瘤上的處理步驟，資料獲得後所經過的資料預處理，再做基因排序特徵減縮，接著以當前性能較好的四種分類方法: 如KNN 法、支持向量機法、貝氏分類法、以及冗餘度分析法，對它們進行了集成學習分類法研究，實驗結果顯示集成分類法是當前分類結果較為穩定，準確度較高及性能較好的ㄧ種方法。 Cancer has been ranked on the top one cause of death for many years. Currently most cases of cancer are not discovered until the latter stages because it usually causes no symptoms. Surgery is especially important in the early stages of cancer development but not the final stage. The traditional tumor morphology is classified by pathologist on the approach that is for some organisms in the early stages of cancer development. However, this classification method cannot effectively discriminate terminal cancer that has similar pathological appearance but different course and prognosis. Today, gene chips, an important means of molecular diagnosis, can be used to track and monitor large data sets covering the expression of thousands of genes over a wide range of different organisms’ tissues/samples. It can not only facilitate in tumor classification but also discover in new disease identification. Applying data mining techniques for Leukemia classification from gene expression data through a serial of process are pre-processing, feature reduction and gene ranking. Four classification methods-KNN, SVM, Naïve Bayes’ and RDA, were implemented and compared in this research. Experimental result of this research indicates that Ensemble Approach create a better and stable performance in term of prediction precision.
Appears in Collections:	[Department of Information Management & Graduate Institute of Information Management] Thesis

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