Applying a clustering genetic algorithm for extracting rules from a supervised neural network

The main challenge to the use of supervised neural networks in data mining applications is to get explicit knowledge from these models. For this purpose, a clustering genetic algorithm for rule extraction from artificial neural networks is developed. The methodology is based on the clustering of the hidden units activation values. A simple encoding scheme that yields to constant-length chromosomes is used thus allowing the application of the standard genetic operators. A consistent algorithm to avoid some of the drawbacks of this kind of representation is also developed. In addition, a very simple heuristic is applied to generate the initial population. The individual fitness is determined based on the number of objects belonging to each cluster, as well as on the Euclidean distances among the objects. The developed algorithm is experimentally evaluated in the Australian credit approval database