Neuro-fuzzy networks for pattern classification and rule extraction

Abstract

Summary form only given. An experimental evaluation of the neurofuzzy models NEFCLASS and FuNN is conducted in real world pattern recognition applications. The models are investigated with respect to classification performance and the number of rules generated and compared to the traditional MLP network trained with backpropagation. The models NEFCLASS and FuNN are examined in benchmarking problems from the Proben1 database and in a large-scale credit card screening problem. A comparison is established with an MLP network and the results obtained show some potential advantages of the neuro-fuzzy classifiers over the MLP particularly with respect to the ability of the neuro-fuzzy models to generate a knowledge base of rules.