近年來由於生物晶片(BioChip)技術的快速發展，使得生物學家可以在同一時間內一次分析成千上萬的基因，對於臨床實驗上了解癌症的發生與發展等相關研究提供了一個極佳的解決方案，其中又以具有迅速檢測基因表現之能力的基因微陣列(cDNA MicroArray)技術最受人矚目。雖然基因微陣列可以攜帶豐富的資訊以供研究，但其高維度(dimension)且高雜訊(noise)的資料特性增加了專家學者進行研究時在資料處理程序上的困難度；此外，其樣本取得不易且變數個數(Genes)遠大於資料個數(tissue samples)的現象也帶出了小樣本學習誤差的問題。

基因微陣列資料特性所衍生出來的問題也引起了學者們廣泛的討論，紛紛提出了各種不同的解決方法，這些方法多是針對基因選取(Gene Selection)來做改良。然而去除基因微陣列中大量的雜訊(不重要的基因)雖然可以同時達到降低維度並提高預測正確率的目標，但前提是必須要有足夠的樣本數。當新興的疾病問世，尚未有足夠的患病人數可以採集薄膜樣本時，或者是基於成本或其他因素的考量無法取得足夠的樣本數時，再好的基因選取法也很難有優異的成效。

因此本研究將在基因微陣列的研究領域上加入全新的元素，引進虛擬樣本(Virtual Samples)的概念來處理癌症分類問題。方法上第一階段先以基因排序法找出具鑑別力的基因,第二階段則應用群集化核心密度估計法(Clusterized KDE)產生虛擬樣本以增加有意義的資訊來幫助分類器作正確的預測。預期透過新增的虛擬樣本來增加訓練的樣本空間以提升癌症辨識率。

DNA microarray today plays an important role of the cancer classification problem. Microarray technology allows us to measure the expression levels of thousands of genes simultaneously in clinical experiments. Clinicians are enable to obtain the gene expression profile of tissue samples rapidly and make decision correctly.

DNA microarray data are characterized as low size, high dimensionality (this is called the small samples problem), a large number of noise or high correlation genes. Recently researchers apply gene selection mechanism to find the genes most relevant to a specific classification task. It can improve learning accuracy and reduce the computation cost, but can not solve the innately limited of lock of training samples, for example, in the early stages, like during the outbreak of the new disease, only limited data can be obtained, so that the model derived is also too unstable to deal with the new disease effectively, and the performance can not improve significant even thought using gene selection mechanism.

In this paper, we propose the virtual sample technique, named CKDE (Clusterized Kernel Density Estimation). Not only apply gene selection mechanism but then analysis the characteristic of data after reduced. Generate the virtual sample to increase meaningful information, and the proposed model improves the learning accuracy significantly.

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