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Hierarchical Parallel Genetic Optimization Fuzzy ARTMAP Ensemble

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Abstract

In this paper, a framework for designing optimum pattern classifiers is proposed. The fuzzy ARTMAP (FAM) is first used as a base classifier. Multiple FAM classifiers form an ensemble to improve classification accuracy. Multi-objective genetic algorithms (GAs) are then used to search for the best combinations of variables, for the FAM classifiers. Based on the population of potential solutions, another GA selects the best combination of FAM classifiers to create an ensemble. Individual decisions are combined using a probabilistic voting scheme. To increase the inter-classifier diversity, a hierarchical parallel GA variant and a negative correlation method is employed during the genetic optimization phase for the ensemble evaluation. The proposed framework is evaluated using benchmark and real-world data sets, and the results compared with literature. Results positively indicate the proposed framework is effective in undertaking data classification tasks.

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Acknowledgments

This research is supported by University of Malaya High Impact Research Grant UM.C/625/1/HIR/MOHE /FCSIT/10.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Wei Shiung Liew & Chu Kiong Loo

  2. Faculty of Engineering, Computing and Science, Swinburne University of Technology (Sarawak Campus), Kuching, Sarawak, Malaysia

    Manjeevan Seera

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  1. Wei Shiung Liew
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  2. Manjeevan Seera
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  3. Chu Kiong Loo
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Correspondence to Manjeevan Seera.

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Liew, W.S., Seera, M. & Loo, C.K. Hierarchical Parallel Genetic Optimization Fuzzy ARTMAP Ensemble. Neural Process Lett 44, 451–470 (2016). https://doi.org/10.1007/s11063-015-9467-9

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