Rule Extraction From Neural Networks In DataMining Applications
Eduardo R. Hruschka · WIT transactions on information and communication technologies · 1970
This work deals with the efficient discovery of valuable and nonobvious information from large collections of data, using Computacional Intelligence tools. For this purpose, a . study about knowledge acquirement from supervised neural networks employed for classification problems is presented. An algorithm for rule extraction from neural networks, based on the work by Lu et al. [1] in 1996, is developed. This algorithm, named Modified RX, is experimentally evaluated in three different domains. The results are compared to those obtained by classification trees. In respect of the efficacy , one observes that the successful application of the algorithm mainly depends on the knowledge representation acquired by the conecctionist model, while the eflcciency only depends on the neural network training time.