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

Autor:	Gi Joon Jeon, Young-Wung Kim, Guk-Hee Kim, Sang Jin Lee
Rok vydání:	2010
Předmět:	Engineering Electronic nose Structured support vector machine business.industry Pattern recognition computer.software_genre Fuzzy logic Hierarchical clustering Support vector machine ComputingMethodologies_PATTERNRECOGNITION Data mining Artificial intelligence business computer
Zdroj:	Journal of Korean Institute of Intelligent Systems. 20:716-721
ISSN:	1976-9172
DOI:	10.5391/jkiis.2010.20.5.716
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3b01edefd94a116ea9ffb0680567bab6 https://doi.org/10.5391/jkiis.2010.20.5.716 Zobrazit plný text záznamu