Extracao de regras de redes neurais via algoritmos geneticos

In kwonledge extraction from databases, a frequently found problem is noisy data. Neural networks are usually tolerant a noise in the training set, but they are have a poor performance in explaining how a solution is found. In this work it is presented a method for obtainiy correct and comprehensible kwonledge by using rule extraction from trained neural networks. Such a method uses genetic algorithm to find a suitable topology for a neural network that allows the RX algorithm to extract a rule set as accurate and comprehensible as possible. The proposed system was tested with two publicly available data sets and results were very satisfactory.