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Classification of Incomplete Data Based on Evidence Theory and an Extreme Learning Machine in Wireless Sensor Networks

In wireless sensor networks, the classification of incomplete data reported by sensor nodes is an open issue because it is difficult to accurately estimate the missing values. In many cases, the misclassification is unacceptable considering that it probably brings catastrophic damages to the data us...

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Detalles Bibliográficos
Autores principales: Zhang, Yang, Liu, Yun, Chao, Han-Chieh, Zhang, Zhenjiang, Zhang, Zhiyuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948797/
https://www.ncbi.nlm.nih.gov/pubmed/29601552
http://dx.doi.org/10.3390/s18041046
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author Zhang, Yang
Liu, Yun
Chao, Han-Chieh
Zhang, Zhenjiang
Zhang, Zhiyuan
author_facet Zhang, Yang
Liu, Yun
Chao, Han-Chieh
Zhang, Zhenjiang
Zhang, Zhiyuan
author_sort Zhang, Yang
collection PubMed
description In wireless sensor networks, the classification of incomplete data reported by sensor nodes is an open issue because it is difficult to accurately estimate the missing values. In many cases, the misclassification is unacceptable considering that it probably brings catastrophic damages to the data users. In this paper, a novel classification approach of incomplete data is proposed to reduce the misclassification errors. This method uses the regularized extreme learning machine to estimate the potential values of missing data at first, and then it converts the estimations into multiple classification results on the basis of the distance between interval numbers. Finally, an evidential reasoning rule is adopted to fuse these classification results. The final decision is made according to the combined basic belief assignment. The experimental results show that this method has better performance than other traditional classification methods of incomplete data.
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spelling pubmed-59487972018-05-17 Classification of Incomplete Data Based on Evidence Theory and an Extreme Learning Machine in Wireless Sensor Networks Zhang, Yang Liu, Yun Chao, Han-Chieh Zhang, Zhenjiang Zhang, Zhiyuan Sensors (Basel) Article In wireless sensor networks, the classification of incomplete data reported by sensor nodes is an open issue because it is difficult to accurately estimate the missing values. In many cases, the misclassification is unacceptable considering that it probably brings catastrophic damages to the data users. In this paper, a novel classification approach of incomplete data is proposed to reduce the misclassification errors. This method uses the regularized extreme learning machine to estimate the potential values of missing data at first, and then it converts the estimations into multiple classification results on the basis of the distance between interval numbers. Finally, an evidential reasoning rule is adopted to fuse these classification results. The final decision is made according to the combined basic belief assignment. The experimental results show that this method has better performance than other traditional classification methods of incomplete data. MDPI 2018-03-30 /pmc/articles/PMC5948797/ /pubmed/29601552 http://dx.doi.org/10.3390/s18041046 Text en © 2018 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
Zhang, Yang
Liu, Yun
Chao, Han-Chieh
Zhang, Zhenjiang
Zhang, Zhiyuan
Classification of Incomplete Data Based on Evidence Theory and an Extreme Learning Machine in Wireless Sensor Networks
title Classification of Incomplete Data Based on Evidence Theory and an Extreme Learning Machine in Wireless Sensor Networks
title_full Classification of Incomplete Data Based on Evidence Theory and an Extreme Learning Machine in Wireless Sensor Networks
title_fullStr Classification of Incomplete Data Based on Evidence Theory and an Extreme Learning Machine in Wireless Sensor Networks
title_full_unstemmed Classification of Incomplete Data Based on Evidence Theory and an Extreme Learning Machine in Wireless Sensor Networks
title_short Classification of Incomplete Data Based on Evidence Theory and an Extreme Learning Machine in Wireless Sensor Networks
title_sort classification of incomplete data based on evidence theory and an extreme learning machine in wireless sensor networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948797/
https://www.ncbi.nlm.nih.gov/pubmed/29601552
http://dx.doi.org/10.3390/s18041046
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