摘要
　　隨著資訊技術的發展，基因表現資料 (gene expression data) 成為研究癌症分類(classification) 的一大關鍵，而近年來最引人注目的即是基因微陣列 (microarray) 這項可同時分析上千個基因的技術的發展，這項技術也使得癌症分類的研究邁入一個新的紀元。由於擁有如此豐富的基因表現資料，因此許多研究學者紛紛提出解決癌症分類的各種方法，其中衍生出處理小樣本高維度資料的二階段分類法，所謂二階段分類法即是先從數千個基因中選取與該癌症相關的若干基因再進行分類，在本論文先針對一些學者所提出的解決方法和相關研究做一概括性的說明，做一系列的評估之後以找出最有效能的分類預測的組合。在以往的相關研究往往只討論分類法的優劣，而沒有對挑出的基因群進行分析，本研究利用兩類基因選取法：一類是集合基因排序法；另一類是個別基因排序法共4種基因選取的方法挑選基因，另外再結合其中屬於個別基因排序法來做基因選取，總共5種基因選取法和K鄰近點分類法做結合來分類，以及降低維度後和邏輯斯區別分析做結合來分類。結果發現在任兩個基因選取方式所挑出的基因組合，其相似度都很低，但是在精確度的表現除了屬於集合基因排序法結合K鄰近點分類所得精確度有較好的表現外，其餘精確度的表現都差不多。另外用基因微陣列資料來進行癌症分類，並不是選取愈多的基因變數就能使二階段分類預測的結果有較高精確度。最後經過本研究所提之組合選取法結合分類法的精確度表現可知，如果只是找出每個個別基因排序法前幾名或是都有出現的基因來做基因挑選的改善其進步空間有限。

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