An Association Classification Rule Based Rule extraction Algorithm for Competitive Learning Neural Networks

Neural networks have been successfully applied to solve a variety of application problems including classification and function approximation. They are especially useful for function approximation problems because they have been shown to be universal approximators. But, The neural network is considered a black box. It is hard to determine if the learning result of a neural network is reasonable, and the network can not effectively help users to develop the domain knowledge. Thus, it is important to supply a reasonable and effective analytic method of the neural network. This research expects to be able to improve the black box shortcoming of the solving type neural network.
Competitive Learning Neural Network include Self-Organized Map, Learning Vector Quantization. These common characteristics of network are that are all to adopt the single layer of neural networks that Winner-Take-All completely that their study rules .However, past researchs are mostly all limited on the neural network structure of the feedforward network, but the important degree that can't know this rule. So this research develop to extract out the Association Classification Rule from neurons. Finally, extracted rule is compared decision tree-C4.5, proves with some BenchMark Problems in UCI Machine Learning DataBase that distinguish the correct rate.