Affect classification using genetic-optimized ensembles of fuzzy ARTMAPs

Liew, W.S. and Seera, M. and Loo, C.K. and Lim, E. (2015) Affect classification using genetic-optimized ensembles of fuzzy ARTMAPs. Applied Soft Computing, 27. pp. 53-63. ISSN 1568-4946, DOI https://doi.org/10.1016/j.asoc.2014.10.032.

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Abstract

Training neural networks in distinguishing different emotions from physiological signals frequently involves fuzzy definitions of each affective state. In addition, manual design of classification tasks often uses sub-optimum classifier parameter settings, leading to average classification performance. In this study, an attempt to create a framework for multi-layered optimization of an ensemble of classifiers to maximize the system's ability to learn and classify affect, and to minimize human involvement in setting optimum parameters for the classification system is proposed. Using fuzzy adaptive resonance theory mapping (ARTMAP) as the classifier template, genetic algorithms (GAs) were employed to perform exhaustive search for the best combination of parameter settings for individual classifier performance. Speciation was implemented using subset selection of classification data attributes, as well as using an island model genetic algorithms method. Subsequently, the generated population of optimum classifier configurations was used as candidates to form an ensemble of classifiers. Another set of GAs were used to search for the combination of classifiers that would result in the best classification ensemble accuracy. The proposed methodology was tested using two affective data sets and was able to produce relatively small ensembles of fuzzy ARTMAPs with excellent affect recognition accuracy. (C) 2014 Elsevier B.V. All rights reserved.

Item Type:	Article
Funders:	University of Malaya Research Grant RG115-12ICT
Additional Information:	Ax3rm Times Cited:0 Cited References Count:51
Uncontrolled Keywords:	Affect recognition, classifier ensemble, fuzzy artmap, genetic algorithm, parameter optimization, supervised learning, particle swarm optimization, polynomial neural-networks, negative correlation, algorithm, classifiers, performance, prediction, diagnosis, selection, patterns,
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Engineering
Depositing User:	Mr Jenal S
Date Deposited:	20 Sep 2015 23:58
Last Modified:	20 Sep 2015 23:58
URI:	http://eprints.um.edu.my/id/eprint/13925

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