Rule extraction by genetic algorithms based on a simplified RBF neural network

As an important task of data mining, extracting rules to represent the concept of numerical data is attracting much attention. In this paper, we propose a novel algorithm to extract rules using genetic algorithms (GA) and the radial basis function (RBF) neural network classifier. The interval for each input in the condition part of each rule is adjusted using GA. The fitness of a chromosome is determined by the accuracy of extracted rules. The decision boundary of rules extracted is in the form of hyper-rectangular. During the training of an RBP neural network, large overlaps between clusters corresponding to the same class is allowed in order to decrease the number of hidden units while maintaining classification accuracy. The weights connecting the hidden units with the output units are then pruned. Our simulations demonstrate that our approach leads to more accurate and concise rules.