由於生物與醫學方面的技術不斷創新改進，專家們發展了一套"基因微陣列"技術，來找尋特定基因與疾病的相關性。然而基因微陣列具有高維度且小樣本的特性，其中包含了許多與疾病並不相干的基因，所以目前大多都會針對基因微陣列資料進行前處理以降低維度，而此過程即稱為基因選取。由於現實的癌症問題中，不同類別之分類錯誤成本並不相同，所以過去如成本敏感的研究中，即會在分類階段設定不同類別有不同的成本。在基因選取階段考量分類錯誤成本時，由於完全以成本為優先考量，致使低成本類別之分類正確率大幅滑落，進而造成有時分類錯誤成本不降反升的現象。所以綜合以上理由，本研究提出二階段基因選取法來進行基因選取，其中分別以正確率導向之基因選取法及成本導向之基因選取法進行組合，產生高正確率導向基因選取法及低成本導向基因選取法兩類基因選取架構，共計有18種二階段基因選取法，針對個別基因進行選取，期望在與成本導向基因選取法比較時，能降低分類錯誤成本，並還能不過度損失低成本類別之正確率。結果顯示，本研究所提出之組合式基因選取法，部分較成本導向基因選取法具有競爭力，其中機率排序法-t值法在正確率曲面下面積及成本曲面下面積之效能表現最為突出。

As the continuous improvement and innovation of bioscience and medical science technologies, scientists developed a series of technologies for gene microarray data to find the relations between diseases and genes. The number of instances in a microar-ray data set is far less than the number of genes in an instances, and lots of genes are irrelevant to a specific disease. Therefore, gene selection is essential to reduce the di-mensionality of a microarray data set. The misclassification costs of different classes are generally different. Previous study performs cost-sensitive gene selection such that the classification accuracy of a microarray data set is greatly reduced. To com-pensate such difficiency, this study considers both misclassification cost and predic-tion accuracy to propose 18 two-stage individual gene selection methods for microar-ray data. The experimental results on eight microarray data sets show that the method adopting probability ranking for misclassification cost in the first stage and t-value ranking for prediction accuracy in the second stage has the best performance evalu-ated by area under cost curve and area under accuracy curve.

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