Robert F. Harrison | PubMed Health

PubMed1997

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.

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PubMed1997

An Incremental Adaptive Network for On-line Supervised Learning and Probability Estimation.

In this paper, a novel hybrid utilization of the Fuzzy ARTMAP (FAM) neural network and the Probabilistic Neural Network (PNN) is proposed for on-line learning and probability estimation tasks. There are two distinct advantages to the hybrid network. First, FAM is used as an underlying clustering algorithm to classify the input patterns into different recognition categories during the learning phase, resulting in a significant reduction in the number of pattern nodes required in the PNN. Second, a non-parametric posterior probability distribution estimation procedure, in accordance with the PNN paradigm (i.e. the Parzen-windows estimator), is employed during the prediction phase, where a probabilistic interpretation corresponding to Bayes decision theory can be provided for the predictions of FAM. In addition, several modifications are proposed to integrate both of the networks effectively into a unified platform for enhancing generalization. This hybrid approach also realizes an incremental learning system in which the necessity to specify a static network configuration a priori is eliminated as the network is able to "grow" to accommodate new input patterns sequentially and can thus operate in non-stationary environments. The performance of the network is evaluated with benchmark classification tasks and the results are compared with other approaches. Simulation results indicate that this hybrid network is capable of achieving a value near to the Bayes optimal classification rate. Copyright 1997 Elsevier Science Ltd.

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