Experiments with Safe mu ARTMAP: Effect of the network parameters on the network performance

    Authors

    M. Y. Zhong; B. Rosander; M. Georgiopoulos; G. C. Anagnostopoulos; M. Mollaghasemi;S. Richie

    Comments

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    Abbreviated Journal Title

    Neural Netw.

    Keywords

    machine learning; classification; ARTMAP; safe mu ARTMAP; parameter; settings; entropy; FUZZY ARTMAP; CLASSIFICATION; Computer Science, Artificial Intelligence

    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 mu ARTMAP. In this paper we present reasonable analytical arguments that demonstrate of how we should choose the range of some of the Safe mu ARTMAP 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 mu ARTMAP network parameters. This feat would allow one to save computations when a good performing Safe mu ARTMAP network is needed to be identified for a new classification problem. Furthermore, we performed an exhaustive experimentation to find the best Safe mu ARTMAP 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 ARTMAR These experimental results allow one to make appropriate statements regarding the pair-wise comparison of a number of ART networks (including Safe mu ARTMAP). (c) 2006 Elsevier Ltd. All rights reserved.

    Journal Title

    Neural Networks

    Volume

    20

    Issue/Number

    2

    Publication Date

    1-1-2007

    Document Type

    Article

    Language

    English

    First Page

    245

    Last Page

    259

    WOS Identifier

    WOS:000245343000010

    ISSN

    0893-6080

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