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Sensors 2012, 12(3), 2818-2830; doi:10.3390/s120302818
Article

Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

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, 1,*  and 2
Received: 26 December 2011; in revised form: 12 February 2012 / Accepted: 24 February 2012 / Published: 1 March 2012
(This article belongs to the Section Physical Sensors)
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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 artificial neural network; olfactory model; feature selection; principal component analysis; pattern classification; electronic nose
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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.

AMA 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(3):2818-2830.

Chicago/Turabian Style

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.



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