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Enhanced Global Best Particle Swarm Classification

Enhanced Global Best Particle Swarm Classification

Nabila Nouaouria, Mounir Boukadoum, Robert Proulx
Copyright: © 2014 |Volume: 6 |Issue: 3 |Pages: 15
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781466656826|DOI: 10.4018/ijssci.2014070101
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MLA

Nouaouria, Nabila, et al. "Enhanced Global Best Particle Swarm Classification." IJSSCI vol.6, no.3 2014: pp.1-15. http://doi.org/10.4018/ijssci.2014070101

APA

Nouaouria, N., Boukadoum, M., & Proulx, R. (2014). Enhanced Global Best Particle Swarm Classification. International Journal of Software Science and Computational Intelligence (IJSSCI), 6(3), 1-15. http://doi.org/10.4018/ijssci.2014070101

Chicago

Nouaouria, Nabila, Mounir Boukadoum, and Robert Proulx. "Enhanced Global Best Particle Swarm Classification," International Journal of Software Science and Computational Intelligence (IJSSCI) 6, no.3: 1-15. http://doi.org/10.4018/ijssci.2014070101

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Abstract

Particle Swarm Classification (PSC) is a derivative of Particle Swarm Optimization (PSO) based on the retrieval of the best particle positions corresponding to the centroids of classes. This paper addresses how the position update mechanisms impacts the accuracy of a global best PSC approach. The authors present two variants of the PSC algorithm with different position update mechanisms. In particular, the authors show how the combination of a good parameters tuning, a particle confinement to the search space and a biologically inspired wind dispersion mechanism for them improves the covering quality of search space and thus the classification accuracy of the basic global PSC algorithm. An experimental set up was realized and tested on five benchmark databases, leading to better recognition accuracies than those obtained with the previous PSC algorithm.

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