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A novel Euclidean quality threshold ARTMAP network and its application to pattern classification

  • KES 2008
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Abstract

This paper introduces a novel neural network model known as the Euclidean quality threshold ARTMAP (EQTAM) network and its application to pattern classification. The model is constructed based on fuzzy ARTMAP (FAM) and the quality threshold clustering principle. The main objective of EQTAM is to overcome the effects of training data sequences on FAM and, at the same time, to improve its classification performance. Several artificial data sets and benchmark medical data sets are used to evaluate the effectiveness of the proposed model. Performance comparisons between EQTAM and ARTMAP-based as well as other classifiers are made. From the experimental results, it can be observed that EQTAM is able to produce good results. More importantly, the performance of EQTAM is robust against the effect of training data orders or sequences.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, University of South Australia, Mawson Lakes, SA, 5095, Australia

    Shahrul Nizam Yaakob & Lakhmi Jain

  2. School of Electrical and Electronic Engineering, University of Science Malaysia, Penang, Malaysia

    C. P. Lim

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  1. Shahrul Nizam Yaakob
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  2. C. P. Lim
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  3. Lakhmi Jain
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Correspondence to Shahrul Nizam Yaakob.

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Yaakob, S.N., Lim, C.P. & Jain, L. A novel Euclidean quality threshold ARTMAP network and its application to pattern classification. Neural Comput & Applic 19, 227–236 (2010). https://doi.org/10.1007/s00521-009-0293-8

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  • DOI: https://doi.org/10.1007/s00521-009-0293-8

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