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Least Square Support Vector Machines in Combination with Principal Component Analysis for Electronic Nose Data Classification

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4 Author(s)
Xiaodong Wang ; Dept. of Electron. Eng., Zhejiang Normal Univ., Jinhua, China ; Jianli Chang ; Ke Wang ; Meiying Ye

In this paper, an electronic nose data classification approach based on least square support vector machines (LS-SVM) in combination with principal component analysis (PCA) is investigated. The electronic nose data are first converted into PCA, where the data are projected from a high dimensional space into a low dimensional space, preferably two or three dimensions. Then the resulting features from the PCA are sent into the LS-SVM classifier in order to recognize the gas category. The performance of the proposed approach is validated by cross-validation technique. An experiment has been demonstrated by using coffee data from different types of coffee blends. Experimental results show that the LS-SVM in combination with PCA is an effective technique for the classification of electronic nose data.

Published in:

2009 Second International Symposium on Information Science and Engineering

Date of Conference:

26-28 Dec. 2009