隨著資訊技術的發展，基因表現資料成為研究癌症分類的一大關鍵，而近年來最引人注目的即是基因微陣列(DNA microarray)這項可同時針對上千個基因進行分析的技術，也使得對於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，衍生出處理小樣本高維度資料的二階段分類法，因此許多研究學者紛紛提出解決癌症分類的各種方法，但在這些方法中，我們無法很確切的了解哪一個方法在癌症分類問題的表現上是較佳的。為了對癌症分類問題有更深的了解，本篇論文將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，最重要的部分則是發展一個評估的準則來衡量這些方法的效能，而這樣的評估法可以考慮到分類法的預測精確度及執行時間。
　　本篇論文中，針對小樣本高維度資料的二階段分類法應用在癌症分類上，作廣泛的相關文獻的探討，並且發展出重複10次的3-fold交互認證法則衡量腫瘤類別的預測精確度，再加入計算執行時間的部份，獲得以下結論：1.簡單的基因選取法縮短執行時間，卻降低了預測精確度；2.複雜的基因選取法雖然花費較高的時間，也確實的改善了預測精確度；3.並非選取的變數越多，分類法所得的精確度就越高；4.細胞內大部分的基因對癌症分類的預測來說都是雜訊；5.複雜的基因選取在精確度的改良上不明顯；6.降低維度會造成資訊的損失。最後對兩種不同的二階段分類法做交叉的分析，在這樣的過程中可以有一套標準去衡量分類法的效能。

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中文

陳順宇 (2000)，多變量分析，華泰書局 二版。
陳健尉 (2000)，基因微陣列顯色分析法之簡介及其應用: 二十一世紀基因分析的利器，生物醫學報導，第二期。

英文

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