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Sensors (Basel)

Title:		Enhancing Electronic Nose Performance by Feature Selection Using an Improved Grey Wolf Optimization Based Algorithm
Author(s):		Zhang C; Wang W; Pan Y;
Address:		"Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China. State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China"
Journal Title:		Sensors (Basel)
Year:		2020
Volume:		20200722
Issue:		15
Page Number:		-
DOI:		10.3390/s20154065
ISSN/ISBN:		1424-8220 (Electronic) 1424-8220 (Linking)
Abstract:		"Electronic nose is a kind of widely-used artificial olfactory system for the detection and classification of volatile organic compounds. The high dimensionality of data collected by electronic noses can hinder the process of pattern recognition. Thus, the feature selection is an essential stage in building a robust and accurate model for gas recognition. This paper proposed an improved grey wolf optimizer (GWO) based algorithm for feature selection and applied it on electronic nose data for the first time. Two mechanisms are employed for the proposed algorithm. The first mechanism contains two novel binary transform approaches, which are used for searching feature subset from electronic nose data that maximizing the classification accuracy while minimizing the number of features. The second mechanism is based on the adaptive restart approach, which attempts to further enhance the search capability and stability of the algorithm. The proposed algorithm is compared with five efficient feature selection algorithms on three electronic nose data sets. Three classifiers and multiple assessment indicators are used to evaluate the performance of algorithm. The experimental results show that the proposed algorithm can effectively select the feature subsets that are conducive to gas recognition, which can improve the performance of the electronic nose"
Keywords:		classification electronic nose feature selection grey wolf optimization wrapper method;
Notes:		"PubMed-not-MEDLINEZhang, Chao Wang, Wen Pan, Yong eng U1837209/National Natural Science Foundation of China/ 11774381/National Natural Science Foundation of China/ QYZDY-SSW-JSC007/Key Research Program of the Chinese Academy of Sciences/ Switzerland 2020/07/28 Sensors (Basel). 2020 Jul 22; 20(15):4065. doi: 10.3390/s20154065"