Improving a neuro-fuzzy classifier using exploratory factor analysis

As of this writing, there exists a large variety of recently developed pattern classification methods coming from the domain of machine learning and artificial intelligence. In this paper, we study the performance of a recently developed and improved classifier that integrates fuzzy set theory in a neural network (NEFCLASS). The performance of NEFCLASS is compared to a well-known classification technique from machine learning (C4.5). Both C4.5 and NEFCLASS will be evaluated on a collection of benchmarking data sets. Further, to boost performance of NEFCLASS, we investigate the advantage of preprocessing the algorithm by means of an exploratory factor analysis. We compare the algorithms before and after applying an exploratory factor analysis on leading performance indicators, as there are the accuracy of the created classifier and the magnitude of the associated rule base. (C) 2000 John Wiley & Sons, Inc.