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Metal Oxide Gas Sensor Drift Compensation Using a Two-Dimensional Classifier Ensemble
Sensor drift is the most challenging problem in gas sensing at present. We propose a novel two-dimensional classifier ensemble strategy to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. This strategy is appropriate for mul...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4481969/ https://www.ncbi.nlm.nih.gov/pubmed/25942640 http://dx.doi.org/10.3390/s150510180 |
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author | Liu, Hang Chu, Renzhi Tang, Zhenan |
author_facet | Liu, Hang Chu, Renzhi Tang, Zhenan |
author_sort | Liu, Hang |
collection | PubMed |
description | Sensor drift is the most challenging problem in gas sensing at present. We propose a novel two-dimensional classifier ensemble strategy to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. This strategy is appropriate for multi-class classifiers that consist of combinations of pairwise classifiers, such as support vector machines. We compare the performance of the strategy with those of competing methods in an experiment based on a public dataset that was compiled over a period of three years. The experimental results demonstrate that the two-dimensional ensemble outperforms the other methods considered. Furthermore, we propose a pre-aging process inspired by that applied to the sensors to improve the stability of the classifier ensemble. The experimental results demonstrate that the weight of each multi-class classifier model in the ensemble remains fairly static before and after the addition of new classifier models to the ensemble, when a pre-aging procedure is applied. |
format | Online Article Text |
id | pubmed-4481969 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-44819692015-06-29 Metal Oxide Gas Sensor Drift Compensation Using a Two-Dimensional Classifier Ensemble Liu, Hang Chu, Renzhi Tang, Zhenan Sensors (Basel) Article Sensor drift is the most challenging problem in gas sensing at present. We propose a novel two-dimensional classifier ensemble strategy to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. This strategy is appropriate for multi-class classifiers that consist of combinations of pairwise classifiers, such as support vector machines. We compare the performance of the strategy with those of competing methods in an experiment based on a public dataset that was compiled over a period of three years. The experimental results demonstrate that the two-dimensional ensemble outperforms the other methods considered. Furthermore, we propose a pre-aging process inspired by that applied to the sensors to improve the stability of the classifier ensemble. The experimental results demonstrate that the weight of each multi-class classifier model in the ensemble remains fairly static before and after the addition of new classifier models to the ensemble, when a pre-aging procedure is applied. MDPI 2015-04-30 /pmc/articles/PMC4481969/ /pubmed/25942640 http://dx.doi.org/10.3390/s150510180 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Hang Chu, Renzhi Tang, Zhenan Metal Oxide Gas Sensor Drift Compensation Using a Two-Dimensional Classifier Ensemble |
title | Metal Oxide Gas Sensor Drift Compensation Using a Two-Dimensional Classifier Ensemble |
title_full | Metal Oxide Gas Sensor Drift Compensation Using a Two-Dimensional Classifier Ensemble |
title_fullStr | Metal Oxide Gas Sensor Drift Compensation Using a Two-Dimensional Classifier Ensemble |
title_full_unstemmed | Metal Oxide Gas Sensor Drift Compensation Using a Two-Dimensional Classifier Ensemble |
title_short | Metal Oxide Gas Sensor Drift Compensation Using a Two-Dimensional Classifier Ensemble |
title_sort | metal oxide gas sensor drift compensation using a two-dimensional classifier ensemble |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4481969/ https://www.ncbi.nlm.nih.gov/pubmed/25942640 http://dx.doi.org/10.3390/s150510180 |
work_keys_str_mv | AT liuhang metaloxidegassensordriftcompensationusingatwodimensionalclassifierensemble AT churenzhi metaloxidegassensordriftcompensationusingatwodimensionalclassifierensemble AT tangzhenan metaloxidegassensordriftcompensationusingatwodimensionalclassifierensemble |