Optimizing fuzzy ARTMAP ensembles using hierarchical parallel genetic algorithms and negative correlation

Authors:
Chu Kiong Loo;Wei Shiung Liew;Einly Lim
Affiliations:
Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia;Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia;Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Year:
2013

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Abstract

This study demonstrates a system and methods for optimizing a pattern classification task. A genetic algorithm method was employed to optimize a Fuzzy ARTMAP pattern classification task, followed by another genetic algorithm to assemble an ensemble of classifiers. Two parallel tracks were performed in order to assess a diversity-enhanced classifier and ensemble optimization methodology in comparison with a more straightforward method that does not rely on diverse classifiers and ensembles. Ensembles designed with diverse classifiers outperformed diversity-neutral classifiers in 62.50% of the tested cases. Using a negative correlation method to manipulate inter-classifier diversity, diverse ensembles performed better than non-diverse ensembles in 81.25% of the tested cases.