Classification with missing data in a wireless sensor network

被引：0

作者：

Li, YuanYuan ^{[1
]}

Parker, Lynne E. ^{[1
]}

机构：

[1] Univ Tennessee, Distributed Intelligence Lab, Dept Elect Engn & Comp Sci, Knoxville, TN 37996 USA

来源：

PROCEEDINGS IEEE SOUTHEASTCON 2008, VOLS 1 AND 2 | 2008年

关键词：

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

We have developed a novel method to estimate missing observations in wireless sensor networks. We use a hierarchical unsupervised fuzzy ART neural network to represent the data cluster prototypes and describe missing input patterns based on the network. We then estimate missing inputs by a spatial-temporal imputation technique. Our experimental results show that our proposed approach performs better than nine other missing data imputation techniques including moving average and Expectation-Maximization (EM) imputation.

引用

页码：533 / 538

页数：6

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