Modified Fuzzy ARTMAP Approaches Bayes Optimal Classification Rates: An Empirical Demonstration

This paper investigates the effectiveness of the Fuzzy ARTMAP (FAM) neural network in classifying statistical data and compares the results with Bayesian decision theory. Binary classification problems are used to assess the performance of FAM operating autonomously and on-line in statistical settings. The results illustrate the limitations of FAM in this context. Novel modifications are, therefore, proposed for the category formation process and the category selection process of FAM, which allow the modified system to minimize the misclassification rates. A number of simulations with randomly generated data sets have been carried out. First, two continuous-valued Gaussian sources are used with various source (mean) separations, prior probabilities, and variances. Then, multi-dimensional discrete patterns are employed to examine the classification ability of modified FAM in both stationary and non-stationary environments. Simulation results consistently demonstrate that modified FAM is able to approach the Bayes optimal classification rates on-line, and thereby justify the rationale behind the modifications. Copyright 1997 Elsevier Science Ltd.