Zhang Yang, Liu Yun, Chao Han-Chieh, Zhang Zhenjiang, Zhang Zhiyuan
Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
School of Information Science and Engineering, Fujian University of Technology, Fuzhou 350118, China.
Sensors (Basel). 2018 Mar 30;18(4):1046. doi: 10.3390/s18041046.
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.
在无线传感器网络中，传感器节点报告的不完整数据分类是一个开放性问题，因为难以准确估计缺失值。在许多情况下，考虑到误分类可能给数据用户带来灾难性损害，这种误分类是不可接受的。本文提出了一种新的不完整数据分类方法以减少误分类误差。该方法首先使用正则化极限学习机估计缺失数据的潜在值，然后基于区间数之间的距离将这些估计值转换为多个分类结果。最后，采用证据推理规则融合这些分类结果。根据组合后的基本置信分配做出最终决策。实验结果表明，该方法比其他传统的不完整数据分类方法具有更好的性能。
