英文摘要 |
Neural network, as a popular approach in data mining, usually has better learning results with relatively high accuracy. It provides good fau1t-tolerant ability for handling data with noises, and its network structure can also presents the complicated relationships among attributes. However, such black-boxed type of neural network process lacks the ability of explanation to offer the users with comprehensibly manageable knowledge, and the applications of neural network are occasionally restricted. In this paper, a rule induction algorithm is employed to retrieve the explicit rules for interpret the learning results from neural networks. Furthermore, by considering the misclassification costs in the retrieval process, the retrieved rules would be more realistic to practical uses. The proposed approach is based on PRISM algorithm proposed by Cendrowska, and uses the methods of Adacost, Metacost, and information entropy to consider the misclassification costs. An empirical investigation is performed by utilizing the UCI-ML database to verify the effectiveness of the proposed approach. |