類神經網路為處理資料探勘問題的技術之一，其學習結果通常有較高的正確率，且對於存有雜訊的資料有較好的容錯能力，其網路架構也能夠表達屬性間複雜的關係。然而其學習結果為一黑箱，對於使用者缺乏解釋能力，使得類神經網路在應用上受到一定程度的限制。本研究透過規則歸納演算法由已訓練類神經網路中擷取出明確的規則，用以解釋類神經網路的學習結果，且所提出之規則擷取架構將能夠適用於不同的類神經網路模式中。並於規則擷取的過程考量分類錯誤成本（Misclassification Cost）的影響，使所擷取之規則能反應不同類別的分類錯誤成本更能符合實務上的需要。本研究架構以Cendrowska所提出之PRISM演算法為規則擷取基礎，分別以Adacost、Metacost以及修改PRISM資訊函數三種方式使所擷取之規則能考量分類錯誤成本。並將本研究方法與Zhou等人所提出之規則擷取架構REFNE，以UCI-ML資料庫為評比基礎就所產生規則之規則數目、正確率以及分類錯誤成本進行比較與分析。

　　Neural network is the technology used in solving data mining problem. Usually , its result has higher accuracy and handle well in noise data ,also the network architecture can present the complex relation between attributes .However its learning result is a black box .User can’t easily understand neural network’s learning result which limit the application of neural networl. This research try to explain the learning result of neural network by extracting rules from trained neural network . The extracting architecture can be used in various kind of neural network model .We put the effect of misclassification cost into consideration during the rule extracting process .The architecture of our research is based on PRISM which is proposed by Cendrowska.We combined the modified PRISM model with Adacost ,Metacost and Information cost function in our model to make the whole extracting architecture can consider the effect of misclassification cost .The architecture proposed in the research will compare the number of extracting rule ,accuracy of ruleset and misclassification cost with REFNE which is proposed by Zhou. The comparison is based on UCI-ML dataset .

葉怡成.(民88) 類神經網路模式應用與實作，儒林，台北市.
羅華強.(民90) 類神經網路Matlab的應用，清蔚科技，新竹市.
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