Abstract
To improve the performance of classification algorithms, we proposed a new varianceconsidered machine (VCM) classification algorithm in a previous study. The study showed theoretically that VCMs have lower error probabilities than SVMs. The purpose of this paper is to experimentally demonstrate the superiority of VCMs. Therefore, we verified our proposal with several case experiments using data following a Gaussian distribution with different variances and prior probabilities. To estimate performance, the experiment for each case was executed 1000 times and the error rates were averaged for accuracy. The data of each experiment have different distances between means of data, and different ratios between training data and testing data. Thus, we proved that the error rate of VCMs is lower than the error rate of SVMs, although their performances were not similar in each case. Consequently, we expect that VCMs will be applied to a variety fields.
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Recommended by Editor Young-Hoon Joo. This research was supported by the Chung-Ang University Scholarship Research Grants in 2010.
Hong-Gi Yeom received his B.S. and M.S. degrees in the School of Electrical and Electronics Engineering from Chung-Ang University in 2008 and 2010, respectively. His research interests include brain-computer interface, functional electrical stimulation, machine learning, wearable robot etc.
Seung-Min Park received his B.S. degree in the School of Electrical and Electronics Engineering from Chung-Ang University in 2010. He is currently in Master course in the School of Electrical and Electronics Engineering from Chung-Ang University, Korea. His research interests include brain-computer interface, intention recognition, pattern recognition, soft computing etc.
Junheong Park received his B.S. degree in the School of Electrical and Electronics Engineering from Chung-Ang University in 2011. He is currently in Master course in the School of Electrical and Electronics Engineering from Chung-Ang University, Korea. His research interests include brain-computer interface, image processing, pattern recognition etc.
Kwee-Bo Sim received his B.S. and M.S. degrees in the Department of Electronic Engineering from Chung-Ang University, Korea in 1984 and 1986 respectively, and his Ph.D. degree in the Department of Electrical Engineering from the University of Tokyo, Japan, in 1990. Since 1991, he has been a faculty member of the School of Electrical and Electronics Engineering at Chung-Ang University, where he is currently a Professor. His research interests are in artificial life, intelligent robot, intelligent system, multi-agent system, distributed autonomous robotic system, machine learning, adaptation algorithm, soft computing(neural network, fuzzy system, evolutionary computation), artificial immune systems, evolvable hardware, artificial brain, intelligent home, home networking, intelligent sensor, and ubiquitous computing etc. He is a member of IEEE, SICE, RSJ, IEICE, KITE, KIEE, KIIS, and ICROS Fellow.
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Yeom, HG., Park, SM., Park, J. et al. Superiority demonstration of variance-considered machines by comparing error rate with support vector machines. Int. J. Control Autom. Syst. 9, 595–600 (2011). https://doi.org/10.1007/s12555-011-0321-1
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DOI: https://doi.org/10.1007/s12555-011-0321-1