在數據分析的領域中，尤其在大數據的領域之中，因常含有相當高維度的預測變數，故特徵選擇是一個很重要的主題。這個主題在半導體的應用上，已經獲得相當豐碩的成果，但在IC基板的應用上，成果就相對顯得貧乏。所以，此次的研究(以IC基板中鑽孔製程為例)將透過以下的試驗方法(含：GR-SNBC (Gain Ratio with Naive Bayes Classifier)、SU-SNBC (Symmetrical Uncer-tainty with Naive Bayes Classifier)與SU-CART (Symmetrical Uncer-tainty with Classification and Regression Tree Classifier))，來建立可應用於IC基板製程時間預測上的一組屬性。最後，此一研究的成果不僅在於，使用資料挖礦的方法，來找出一組具有顯著性，而且可以用來預測的IC基板製程時間的產品特徵屬性；而且，發現若為了縮短製程時間，來自產品結構本身的因子，會比來自產品在生產管理上的因子更具顯著的效果。 / Feature selection is significate subject in domain of data analysis, especially in big-data with a lot of high dimension predictive variables. In semi-conductor field, this subject has already gotten a plenty of achievement, but not in IC-substrate; so in this research for example of drilling operation, through experiments, it builds a group of se-lective features for this field to predict process time, and the methods used are GR-SNBC (Gain Ratio with Naive Bayes Classifier), SU-SNBC (Symmetrical Uncertainty with Naive Bayes Classifier) and SU-CART (Symmetrical Uncertainty with Classification and Regression Tree Classifier). The contributions of this research are not only a selective product characteristics subset suggested to predict process-time in IC-substrate fab via the data-mining methods here, but also an observation that in order to shorten the process time, the factors of product construction weighs more than production management.