Towards the Experimental Evaluation of Novel Supervised Fuzzy Adaptive Resonance Theory for Pattern Classification

This paper presents a comparative analysis of novel supervised fuzzy adaptive resonance theory (SF-ART), multilayer perceptron (MLP) and competitive neural trees (CNeT) Networks over three pattern recognition problems. We have used two well-known patterns (IRIS and Vowel data) and a biological data (hydrogen data) to evaluate and check SF-ART stability, reliability, learning speed and computational load. The comparative tests with IRIS, Vowels and H2 data indicate that the SF-ART is capable to perform with a high classification performance, high learning speed (elapsed time for learning around half second), and very low computational load compared to the well-known neural networks such as MLP and CNeT which need minutes and seconds respectively to learn the training material.