An ordering algorithm for pattern presentation in fuzzy ARTMAP that tends to improve generalization performance

    Authors

    I. Dagher; M. Georgiopoulos; G. L. Heileman;G. Bebis

    Comments

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    Abbreviated Journal Title

    IEEE Trans. Neural Netw.

    Keywords

    fuzzy ARTMAP; generalization; learning; max-min clustering; CLASSIFICATION; ARCHITECTURE; Computer Science, Artificial Intelligence; Computer Science, Hardware &; Architecture; Computer Science, Theory & Methods; Engineering, ; Electrical & Electronic

    Abstract

    In this paper we introduce a procedure, based on the max-min clustering method, that identifies a fixed order of training pattern presentation for fuzzy adaptive resonance theory mapping (ARTMAP). This procedure is referred to as the ordering algorithm, and the combination of this procedure with fuzzy ARTMAP is referred to as ordered fuzzy ARTMAP. Experimental results demonstrate that ordered fuzzy ARTMAP exhibits a generalization performance that is better than the average generalization performance of fuzzy ARTMAP, and: in certain cases as good as, or better than the best fuzzy ARTMAP generalization performance. We abo:calculate the number of operations required by the ordering algorithm and compare it to the number of operations required by the training phase of fuzzy ARTMAP, We show that, under mild assumptions, the number of operations required by the ordering algorithm is a fraction of the number of operations required by fuzzy ARTMAP.

    Journal Title

    Ieee Transactions on Neural Networks

    Volume

    10

    Issue/Number

    4

    Publication Date

    1-1-1999

    Document Type

    Article

    Language

    English

    First Page

    768

    Last Page

    778

    WOS Identifier

    WOS:000081385700004

    ISSN

    1045-9227

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