Rule extraction from neural networks: modified RX algorithm

Abstract

The main challenge in using supervised neural networks in data mining applications is to get explicit knowledge from these models. For this purpose, a study on knowledge acquisition from supervised neural networks employed for classification problems is presented. An algorithm for rule extraction from neural networks, based on the RX algorithm is developed. This algorithm, named modified RX, is experimentally evaluated in two different domains: Iris Plants Database and Pima Indians Diabetes Database. The results are compared to those obtained by classification trees. As far as the efficacy is concerned, one observes that the successful application of the algorithm mainly depends on the knowledge representation acquired by the connectionist model, whereas the efficiency only depends on the neural network training time.