A Hybrid RBF-ART Model and Its Application to Medical Data Classification


Tan, Shing Chiang and Lim, Chee Peng and Watada, Junzo (2013) A Hybrid RBF-ART Model and Its Application to Medical Data Classification. In: Intelligent Decision Technologies. Frontiers in Artificial Intelligence and Applications, 255 . IOS Press, pp. 21-30. ISBN 978-1-61499-264-6

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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.

Item Type: Book Section
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science > QA76.75-76.765 Computer software
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 05 Sep 2014 05:02
Last Modified: 05 Sep 2014 05:02
URII: http://shdl.mmu.edu.my/id/eprint/5721


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