A Clustering Genetic Algorithm For Extracting Rules From Multilayer Perceptrons Trained In Classification Problems

This paper deals with the task of using supervised neural networks in data mining applications. The proposed methodology makes use of a clustering genetic algorithm, which is applied in the hidden units activation space in order to extract rules from multilayer perceptrons trained in classification problems. We illustrate the proposed method by means of two examples: Iris Plants Database and Meteorological dataset.