Using Neural Networks
نویسندگان
چکیده
Classiication is one of the data mining problems receiving great attention recently in the database community. This paper presents an approach to discover symbolic classiication rules using neural networks. Neural networks have not been thought suited for data mining because how the classiications were made is not explicitly stated as symbolic rules that are suitable for veriication or interpretation by humans. With the proposed approach, concise symbolic rules with high accuracy can be extracted from a neural network. The network is rst trained to achieve the required accuracy rate. Redundant connections of the network are then removed by a network pruning algorithm. The activation values of the hidden units in the network are analyzed, and classiication rules are generated using the result of this analysis. The eeectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of standard data mining test problems.
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تاریخ انتشار 1996