Using a clustering genetic algorithm for rule extraction from artificial neural networks

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 study on knowledge acquirement from supervised neural networks employed for classification problems is presented. The methodology is based on the clustering of the hidden units activation values. A clustering genetic algorithm for rule extraction from neural networks is developed. 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 Euclidean distances among the objects, as well as on the number of objects belonging to each cluster. The developed algorithm is experimentally evaluated in two data mining benchmarks: Iris Plants Database and Pima Indians Diabetes Database. The results are compared with those obtained by the Modified RX Algorithm (E.R. Hruschka and N.F.F. Ebecken, 1999), which is also an algorithm for rule extraction from neural networks.