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Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application
College of Computer Science & Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China
College of Informatics and Electronics, Zhejiang University of Science and Technology, Hangzhou 310018, China
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Received: 26 December 2011; in revised form: 12 February 2012 / Accepted: 24 February 2012 / Published: 1 March 2012
Abstract: Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.
Keywords: artificial neural network; olfactory model; feature selection; principal component analysis; pattern classification; electronic nose
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MDPI and ACS Style
Fu, J.; Huang, C.; Xing, J.; Zheng, J. Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application. Sensors 2012, 12, 2818-2830.
Fu J, Huang C, Xing J, Zheng J. Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application. Sensors. 2012; 12(3):2818-2830.
Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao. 2012. "Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application." Sensors 12, no. 3: 2818-2830.