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Research on a Gas Concentration Prediction Algorithm Based on Stacking

Machine learning algorithms play an important role in the detection of toxic, flammable and explosive gases, and they are extremely important for the study of mixed gas classification and concentration prediction methods. To solve the problem of low prediction accuracy of gas concentration regressio...

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Detalles Bibliográficos
Autores principales: Xu, Yonghui, Meng, Ruotong, Zhao, Xi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7956455/
https://www.ncbi.nlm.nih.gov/pubmed/33668797
http://dx.doi.org/10.3390/s21051597
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author Xu, Yonghui
Meng, Ruotong
Zhao, Xi
author_facet Xu, Yonghui
Meng, Ruotong
Zhao, Xi
author_sort Xu, Yonghui
collection PubMed
description Machine learning algorithms play an important role in the detection of toxic, flammable and explosive gases, and they are extremely important for the study of mixed gas classification and concentration prediction methods. To solve the problem of low prediction accuracy of gas concentration regression prediction algorithms, a gas concentration prediction algorithm based on a stacking model is proposed in the current research. In this paper, the stochastic forest, extreme random regression tree and gradient boosting decision tree (GBDT) regression algorithms are selected as the base learning devices and use the stacking algorithm to take the output of each base learning device as input to train a new model to produce a final output. Through the stacking model, the grid search algorithm is studied to automatically optimize the parameters so that the performance of the entire system can reach the optimal parameters. Through experimental simulation, the gas concentration prediction algorithm based on stacking model has better prediction effect than other integrated frame algorithms and the accuracy of mixed gas concentration prediction is improved.
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spelling pubmed-79564552021-03-16 Research on a Gas Concentration Prediction Algorithm Based on Stacking Xu, Yonghui Meng, Ruotong Zhao, Xi Sensors (Basel) Article Machine learning algorithms play an important role in the detection of toxic, flammable and explosive gases, and they are extremely important for the study of mixed gas classification and concentration prediction methods. To solve the problem of low prediction accuracy of gas concentration regression prediction algorithms, a gas concentration prediction algorithm based on a stacking model is proposed in the current research. In this paper, the stochastic forest, extreme random regression tree and gradient boosting decision tree (GBDT) regression algorithms are selected as the base learning devices and use the stacking algorithm to take the output of each base learning device as input to train a new model to produce a final output. Through the stacking model, the grid search algorithm is studied to automatically optimize the parameters so that the performance of the entire system can reach the optimal parameters. Through experimental simulation, the gas concentration prediction algorithm based on stacking model has better prediction effect than other integrated frame algorithms and the accuracy of mixed gas concentration prediction is improved. MDPI 2021-02-25 /pmc/articles/PMC7956455/ /pubmed/33668797 http://dx.doi.org/10.3390/s21051597 Text en © 2021 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 (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xu, Yonghui
Meng, Ruotong
Zhao, Xi
Research on a Gas Concentration Prediction Algorithm Based on Stacking
title Research on a Gas Concentration Prediction Algorithm Based on Stacking
title_full Research on a Gas Concentration Prediction Algorithm Based on Stacking
title_fullStr Research on a Gas Concentration Prediction Algorithm Based on Stacking
title_full_unstemmed Research on a Gas Concentration Prediction Algorithm Based on Stacking
title_short Research on a Gas Concentration Prediction Algorithm Based on Stacking
title_sort research on a gas concentration prediction algorithm based on stacking
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7956455/
https://www.ncbi.nlm.nih.gov/pubmed/33668797
http://dx.doi.org/10.3390/s21051597
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