This paper presents a new method to extract knowledge from existing data sets, that is, to extract symbolic rules using the weights of an Artificial Neural Network. The method has been applied to a neural network with special architecture named Enhanced Neural Network (ENN). This architecture improves the results that have been obtained with multilayer perceptron (MLP). The relationship among the knowledge stored in the weights, the performance of the network and the new implemented algorithm to acquire rules from the weights is explained. The method itself gives a model to follow in the knowledge acquisition with ENN.
['Ana Martínez Blanco', 'Angel Castellanos Peñuela', 'Luis Fernando de Mingo López', 'Arcadio Sotto']
Data Mining with Enhanced Neural Networks-CMMSE