Using two phase approach to extract knowledge from artificial neural networks

According to the understanding problem of artificial neural networks, this paper prvide a new method of extracting rules from trained heural network——two phases algorithm. From hidden layer to output layer, it extracts rules by identifying valid regions in the whole hidden activation space. From input layer to hidden layer, it searches the rules based on the analysis of weights between input nodes and hidden nodes so that all instances covered by these rules generate hidden activation vectors lying in the valid regions. Some experiments have demonstrated that our method generates rules of better performance than the decision tree approach in noisy conditions and the fidelity of the rules extracted from a neural network with distributed representations in our method is higher than that in conventional search based methods, such as KT algorithms.