An Ensemble Method for Missing Data of Environmental Sensor Considering Univariate and Multivariate Characteristics

With rapid urbanization, awareness of environmental pollution is growing rapidly and, accordingly, interest in environmental sensors that measure atmospheric and indoor air quality is increasing. Since these IoT-based environmental sensors are sensitive and value reliability, it is essential to deal...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Chanyoung Choi, Haewoong Jung, Jaehyuk Cho
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	missing data environmental sensor univariate and multivariate imputation machine learning ensemble method Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/f2eff07a07814bb78470a1c93081f821
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:f2eff07a07814bb78470a1c93081f821
record_format	dspace
spelling	oai:doaj.org-article:f2eff07a07814bb78470a1c93081f8212021-11-25T18:57:44ZAn Ensemble Method for Missing Data of Environmental Sensor Considering Univariate and Multivariate Characteristics10.3390/s212275951424-8220https://doaj.org/article/f2eff07a07814bb78470a1c93081f8212021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7595https://doaj.org/toc/1424-8220With rapid urbanization, awareness of environmental pollution is growing rapidly and, accordingly, interest in environmental sensors that measure atmospheric and indoor air quality is increasing. Since these IoT-based environmental sensors are sensitive and value reliability, it is essential to deal with missing values, which are one of the causes of reliability problems. Characteristics that can be used to impute missing values in environmental sensors are the time dependency of single variables and the correlation between multivariate variables. However, in the existing method of imputing missing values, only one characteristic has been used and there has been no case where both characteristics were used. In this work, we introduced a new ensemble imputation method reflecting this. First, the cases in which missing values occur frequently were divided into four cases and were generated into the experimental data: communication error (aperiodic, periodic), sensor error (rapid change, measurement range). To compare the existing method with the proposed method, five methods of univariate imputation and five methods of multivariate imputation—both of which are widely used—were used as a single model to predict missing values for the four cases. The values predicted by a single model were applied to the ensemble method. Among the ensemble methods, the weighted average and stacking methods were used to derive the final predicted values and replace the missing values. Finally, the predicted values, substituted with the original data, were evaluated by a comparison between the mean absolute error (MAE) and the root mean square error (RMSE). The proposed ensemble method generally performed better than the single method. In addition, this method simultaneously considers the correlation between variables and time dependence, which are characteristics that must be considered in the environmental sensor. As a result, our proposed ensemble technique can contribute to the replacement of the missing values generated by environmental sensors, which can help to increase the reliability of environmental sensor data.Chanyoung ChoiHaewoong JungJaehyuk ChoMDPI AGarticlemissing dataenvironmental sensorunivariate and multivariate imputationmachine learningensemble methodChemical technologyTP1-1185ENSensors, Vol 21, Iss 7595, p 7595 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	missing data environmental sensor univariate and multivariate imputation machine learning ensemble method Chemical technology TP1-1185
spellingShingle	missing data environmental sensor univariate and multivariate imputation machine learning ensemble method Chemical technology TP1-1185 Chanyoung Choi Haewoong Jung Jaehyuk Cho An Ensemble Method for Missing Data of Environmental Sensor Considering Univariate and Multivariate Characteristics
description	With rapid urbanization, awareness of environmental pollution is growing rapidly and, accordingly, interest in environmental sensors that measure atmospheric and indoor air quality is increasing. Since these IoT-based environmental sensors are sensitive and value reliability, it is essential to deal with missing values, which are one of the causes of reliability problems. Characteristics that can be used to impute missing values in environmental sensors are the time dependency of single variables and the correlation between multivariate variables. However, in the existing method of imputing missing values, only one characteristic has been used and there has been no case where both characteristics were used. In this work, we introduced a new ensemble imputation method reflecting this. First, the cases in which missing values occur frequently were divided into four cases and were generated into the experimental data: communication error (aperiodic, periodic), sensor error (rapid change, measurement range). To compare the existing method with the proposed method, five methods of univariate imputation and five methods of multivariate imputation—both of which are widely used—were used as a single model to predict missing values for the four cases. The values predicted by a single model were applied to the ensemble method. Among the ensemble methods, the weighted average and stacking methods were used to derive the final predicted values and replace the missing values. Finally, the predicted values, substituted with the original data, were evaluated by a comparison between the mean absolute error (MAE) and the root mean square error (RMSE). The proposed ensemble method generally performed better than the single method. In addition, this method simultaneously considers the correlation between variables and time dependence, which are characteristics that must be considered in the environmental sensor. As a result, our proposed ensemble technique can contribute to the replacement of the missing values generated by environmental sensors, which can help to increase the reliability of environmental sensor data.
format	article
author	Chanyoung Choi Haewoong Jung Jaehyuk Cho
author_facet	Chanyoung Choi Haewoong Jung Jaehyuk Cho
author_sort	Chanyoung Choi
title	An Ensemble Method for Missing Data of Environmental Sensor Considering Univariate and Multivariate Characteristics
title_short	An Ensemble Method for Missing Data of Environmental Sensor Considering Univariate and Multivariate Characteristics
title_full	An Ensemble Method for Missing Data of Environmental Sensor Considering Univariate and Multivariate Characteristics
title_fullStr	An Ensemble Method for Missing Data of Environmental Sensor Considering Univariate and Multivariate Characteristics
title_full_unstemmed	An Ensemble Method for Missing Data of Environmental Sensor Considering Univariate and Multivariate Characteristics
title_sort	ensemble method for missing data of environmental sensor considering univariate and multivariate characteristics
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f2eff07a07814bb78470a1c93081f821
work_keys_str_mv	AT chanyoungchoi anensemblemethodformissingdataofenvironmentalsensorconsideringunivariateandmultivariatecharacteristics AT haewoongjung anensemblemethodformissingdataofenvironmentalsensorconsideringunivariateandmultivariatecharacteristics AT jaehyukcho anensemblemethodformissingdataofenvironmentalsensorconsideringunivariateandmultivariatecharacteristics AT chanyoungchoi ensemblemethodformissingdataofenvironmentalsensorconsideringunivariateandmultivariatecharacteristics AT haewoongjung ensemblemethodformissingdataofenvironmentalsensorconsideringunivariateandmultivariatecharacteristics AT jaehyukcho ensemblemethodformissingdataofenvironmentalsensorconsideringunivariateandmultivariatecharacteristics
_version_	1718410500759355392

An Ensemble Method for Missing Data of Environmental Sensor Considering Univariate and Multivariate Characteristics

Ejemplares similares