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http://dx.doi.org/10.5391/JKIIS.2010.20.5.716

A Hierarchical Clustering Method Based on SVM for Real-time Gas Mixture Classification  

Kim, Guk-Hee (School of Electronics Engineering, College of IT Engineering Kyungpook National University)
Kim, Young-Wung (School of Electronics Engineering, College of IT Engineering Kyungpook National University)
Lee, Sang-Jin (School of Electronics Engineering, College of IT Engineering Kyungpook National University)
Jeon, Gi-Joon (School of Electronics Engineering, College of IT Engineering Kyungpook National University)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.20, no.5, 2010 , pp. 716-721 More about this Journal
Abstract
In this work we address the use of support vector machine (SVM) in the multi-class gas classification system. The objective is to classify single gases and their mixture with a semiconductor-type electronic nose. The SVM has some typical multi-class classification models; One vs. One (OVO) and One vs. All (OVA). However, studies on those models show weaknesses on calculation time, decision time and the reject region. We propose a hierarchical clustering method (HCM) based on the SVM for real-time gas mixture classification. Experimental results show that the proposed method has better performance than the typical multi-class systems based on the SVM, and that the proposed method can classify single gases and their mixture easily and fast in the embedded system compared with BP-MLP and Fuzzy ARTMAP.
Keywords
support vector machine; multi-class system; gas classification; hierarchical clustering method;
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