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Sensors 2018, 18(6), 1909; https://doi.org/10.3390/s18061909

Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework

1
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
2
High Tech Department, China International Engineering Consulting Corporation, Beijing 100048, China
3
Westa College, Southwest University, Chongqing 400715, China
*
Author to whom correspondence should be addressed.
Received: 27 May 2018 / Revised: 6 June 2018 / Accepted: 9 June 2018 / Published: 12 June 2018
(This article belongs to the Section Chemical Sensors)
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Abstract

In this paper, a novel feature selection and fusion framework is proposed to enhance the discrimination ability of gas sensor arrays for odor identification. Firstly, we put forward an efficient feature selection method based on the separability and the dissimilarity to determine the feature selection order for each type of feature when increasing the dimension of selected feature subsets. Secondly, the K-nearest neighbor (KNN) classifier is applied to determine the dimensions of the optimal feature subsets for different types of features. Finally, in the process of establishing features fusion, we come up with a classification dominance feature fusion strategy which conducts an effective basic feature. Experimental results on two datasets show that the recognition rates of Database I and Database II achieve 97.5% and 80.11%, respectively, when k = 1 for KNN classifier and the distance metric is correlation distance (COR), which demonstrates the superiority of the proposed feature selection and fusion framework in representing signal features. The novel feature selection method proposed in this paper can effectively select feature subsets that are conducive to the classification, while the feature fusion framework can fuse various features which describe the different characteristics of sensor signals, for enhancing the discrimination ability of gas sensors and, to a certain extent, suppressing drift effect. View Full-Text
Keywords: electronic nose; feature selection; feature fusion; multiclass recognition; sensor drift electronic nose; feature selection; feature fusion; multiclass recognition; sensor drift
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Deng, C.; Lv, K.; Shi, D.; Yang, B.; Yu, S.; He, Z.; Yan, J. Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework. Sensors 2018, 18, 1909.

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