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.
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Loo, C.K., Liew, W.S., Lim, E. (2013). Optimizing Fuzzy ARTMAP Ensembles Using Hierarchical Parallel Genetic Algorithms and Negative Correlation. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39065-4_49
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DOI: https://doi.org/10.1007/978-3-642-39065-4_49
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