A hybrid RBF-ART model and its application to medical data classification

Shing Chiang Tan*, Chee Peng Lim, Junzo Watada

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)


    In this paper, a new variant of the Radial Basis Function Network with the Dynamic Decay Adjustment algorithm (i.e., RBFNDDA) to undertake data classification problems is proposed. The new network is formed by integrating the learning algorithm of the Fuzzy ARTMAP (FAM) neural network into RBFNDDA. The proposed RBFNDDA-FAM network inherits the salient features of FAM and overcomes the shortcomings of the original RBFNDDA network. The effectiveness of RBFNDDA-FAM is demonstrated using two benchmark problems. The first involves an artificial data set whereas the second uses a medical data set related to thyroid diagnosis. The results from these studies are compared, analyzed, and discussed. The outcomes positively reveal the potentials of RBFNDDA-FAM in learning information with a compact network architecture, in addition to high classification performances.

    Original languageEnglish
    Title of host publicationFrontiers in Artificial Intelligence and Applications
    Number of pages10
    Publication statusPublished - 2013

    Publication series

    NameFrontiers in Artificial Intelligence and Applications
    ISSN (Print)09226389


    • Adaptive resonance theory neural network
    • Classification
    • Clinical decision support
    • Hybrid learning
    • Radial basis function neural network

    ASJC Scopus subject areas

    • Artificial Intelligence


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