Experiments with Safe µARTMAP: Effect of the network parameters on the network performance

Authors:
Mingyu Zhong;Bryan Rosander;Michael Georgiopoulos;Georgios C. Anagnostopoulos;Mansooreh Mollaghasemi;Samuel Richie
Affiliations:
School of EECS, University of Central Florida, Orlando, FL 32816, United States;School of EECS, University of Central Florida, Orlando, FL 32816, United States;School of EECS, University of Central Florida, Orlando, FL 32816, United States;Department of ECE, Florida Institute of Technology, Melbourne, FL 32901, United States;Department of IEMS, University of Central Florida, Orlando, FL 32816, United States;School of EECS, University of Central Florida, Orlando, FL 32816, United States
Venue:
Neural Networks
Year:
2007

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Abstract

Fuzzy ARTMAP (FAM) is currently considered to be one of the premier neural network architectures in solving classification problems. One of the limitations of Fuzzy ARTMAP that has been extensively reported in the literature is the category proliferation problem. That is, Fuzzy ARTMAP has the tendency of increasing its network size, as it is confronted with more and more data, especially if the data are of the noisy and/or overlapping nature. To remedy this problem a number of researchers have designed modifications to the training phase of Fuzzy ARTMAP that had the beneficial effect of reducing this category proliferation. One of these modified Fuzzy ARTMAP architectures was the one proposed by Gomez-Sanchez, and his colleagues, referred to as Safe @mARTMAP. In this paper we present reasonable analytical arguments that demonstrate of how we should choose the range of some of the Safe @mARTMAP network parameters. Through a combination of these analytical arguments and experimentation we were able to identify good default parameter values for some of the Safe @mARTMAP network parameters. This feat would allow one to save computations when a good performing Safe @mARTMAP network is needed to be identified for a new classification problem. Furthermore, we performed an exhaustive experimentation to find the best Safe @mARTMAP network for a variety of problems (simulated and real problems), and we compared it with other best performing ART networks, including other ART networks that claim to resolve the category proliferation problem in Fuzzy ARTMAP. These experimental results allow one to make appropriate statements regarding the pair-wise comparison of a number of ART networks (including Safe @mARTMAP).