ArticleSensors (Basel, Switzerland)2018
Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Improved Skip-Gram Based on Graph Structure Information.Sensors (Basel, Switzerland) · 2023Article
- A preliminary screening system for diabetes based on in-car electronic nose.Endocrine connections · 2023Article
- Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose.Artificial intelligence in medicine · 2022Article
- Review on Smart Gas Sensing Technology.Sensors (Basel, Switzerland) · 2019Review
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Authors and funding
7 authors.
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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
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