Enhancing Electronic Nose Performance by Feature Selection Using an Improved Grey Wolf Optimization Based Algorithm
1
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
2
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China
3
State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(15), 4065; https://doi.org/10.3390/s20154065
Received: 10 June 2020
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Revised: 19 July 2020
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Accepted: 19 July 2020
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Published: 22 July 2020
(This article belongs to the Section Physical Sensors)
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.
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Keywords:
feature selection; grey wolf optimization; electronic nose; classification; wrapper method
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MDPI and ACS Style
Zhang, C.; Wang, W.; Pan, Y. Enhancing Electronic Nose Performance by Feature Selection Using an Improved Grey Wolf Optimization Based Algorithm. Sensors 2020, 20, 4065. https://doi.org/10.3390/s20154065
AMA Style
Zhang C, Wang W, Pan Y. Enhancing Electronic Nose Performance by Feature Selection Using an Improved Grey Wolf Optimization Based Algorithm. Sensors. 2020; 20(15):4065. https://doi.org/10.3390/s20154065
Chicago/Turabian StyleZhang, Chao, Wen Wang, and Yong Pan. 2020. "Enhancing Electronic Nose Performance by Feature Selection Using an Improved Grey Wolf Optimization Based Algorithm" Sensors 20, no. 15: 4065. https://doi.org/10.3390/s20154065
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