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Article

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

1
Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
2
School of Information Science and Engineering, Fujian University of Technology, Fuzhou 350118, China
3
School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430024, China
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Department of Electrical Engineering, National Dong Hwa University, Hualien 97401, Taiwan
5
Department of Computer Science and Information Engineering, National Ilan University, Yilan 26047, Taiwan
6
School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(4), 1046; https://doi.org/10.3390/s18041046
Received: 31 January 2018 / Revised: 27 March 2018 / Accepted: 29 March 2018 / Published: 30 March 2018
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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. View Full-Text
Keywords: classification; incomplete data; evidence theory; extreme learning machine; wireless sensor network classification; incomplete data; evidence theory; extreme learning machine; wireless sensor network
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MDPI and ACS Style

Zhang, Y.; Liu, Y.; Chao, H.-C.; Zhang, Z.; Zhang, Z. Classification of Incomplete Data Based on Evidence Theory and an Extreme Learning Machine in Wireless Sensor Networks. Sensors 2018, 18, 1046. https://doi.org/10.3390/s18041046

AMA Style

Zhang Y, Liu Y, Chao H-C, Zhang Z, Zhang Z. Classification of Incomplete Data Based on Evidence Theory and an Extreme Learning Machine in Wireless Sensor Networks. Sensors. 2018; 18(4):1046. https://doi.org/10.3390/s18041046

Chicago/Turabian Style

Zhang, Yang; Liu, Yun; Chao, Han-Chieh; Zhang, Zhenjiang; Zhang, Zhiyuan. 2018. "Classification of Incomplete Data Based on Evidence Theory and an Extreme Learning Machine in Wireless Sensor Networks" Sensors 18, no. 4: 1046. https://doi.org/10.3390/s18041046

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