Title

Experiments With Safe Μartmap : Effect Of The Network Parameters On The Network Performance

Keywords

ARTMAP; Classification; Entropy; Machine learning; Parameter settings; Safe μARTMAP

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 μARTMAP. In this paper we present reasonable analytical arguments that demonstrate of how we should choose the range of some of the Safe μARTMAP 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 μARTMAP network parameters. This feat would allow one to save computations when a good performing Safe μARTMAP network is needed to be identified for a new classification problem. Furthermore, we performed an exhaustive experimentation to find the best Safe μARTMAP 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 μARTMAP). © 2006 Elsevier Ltd. All rights reserved.

Publication Date

3-1-2007

Publication Title

Neural Networks

Volume

20

Issue

2

Number of Pages

245-259

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.neunet.2006.11.008

Socpus ID

33847146680 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33847146680

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