在生產產品時，維持品質是重要的議題，而統計製程管制是品質管理中常使用的製程分析技術，例如管制圖即為一種相當普遍的管理製程工具，若能妥當的使用資料探勘與機器學習等方法處理此技術產生的資訊，勢必能減少瑕疵產品的製造，達到降低生產成本的效果。在管制圖圖形辨識上，特徵萃取被證實能在管制圖辨識任務中提供有效的分類資訊，但卻少有學者使用特徵萃取在混合管制圖圖形的研究中。因此本研究將結合特徵萃取與特徵選取方法來分類混合管制圖圖形，先針對各類別資料以特徵萃取取得各項特徵，再透過特徵選取找出對該類別最有分類幫助的特徵，將不同的特徵進行組合並訓練分類方法取得分類器，探討特徵對於混合管制圖圖形的分類成效。實驗結果顯示，相對於使用對少數類別有高強度分類能力來組合特徵，使用對多數類別有一般分類能力來組合特徵為較簡單且有效的選擇，而在此特徵組合方向下，組合出的特徵在分類正確率與辨識速度上皆有提升效果，若主要目標若為提升分類正確率，建議需把管制圖圖形觀測值與特徵一同作為資料輸入分類器中，達到顯著提升分類正確率與輕微提升辨識速度的效果；若主要目標為提升辨識速度，則建議只使用特徵作為資料輸入分類器中，透過降低些微的分類正確率來明顯提升辨識速度。

Maintaining good production quality is an important issue in manufacturing industry. Statistical process control is a technique of analyzing manufacturing process, and this technique is commonly used in quality management. Control chart is a fairly common tool in statistical process control, and the occurring probability of defective products and production cost can be reduced if the methods of data mining and machine learning can be properly chosen to analyze the information of control charts. In the research of recognizing control chart patterns, feature extraction has been proven that it could provide effective information of classification, but few scholars have used this technique in recognizing concurrent control chart patterns. Therefore, this study combines feature extraction with feature selection to classify concurrent control chart patterns. In the beginning, feature extraction is used to obtain various features of control chart patterns. Feature selection is then used to find the features which could provide the best classification performance on a specific category of patterns. Several approaches for combining the features for various patterns are proposed to investigate the impact of features on recognizing concurrent control charts. The experimental results show that the combination of the features that are useful for recognizing more patterns will be a better choice for improving recognition accuracy and speed. If the target is to improve classification accuracy, both the observations and the features corresponding to a pattern should be considered. On the contrary, considering only the features corresponding to a pattern is a better way to improve recognition speed.

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