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Computational Intelligence and Neuroscience
Volume 2015 (2015), Article ID 423581, 8 pages
http://dx.doi.org/10.1155/2015/423581
Research Article

Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations

1School of Computer Software, Tianjin University, Tianjin 300072, China
2Centre for Excellence in Signal and Image Processing, University of Strathclyde, Glasgow G1 1XW, UK
3School of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China

Received 24 March 2015; Revised 8 May 2015; Accepted 11 May 2015

Academic Editor: Pietro Aricò

Copyright © 2015 Yi Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.