NDLI: Support Vector Machines Based on K Nearest Neighbor Algorithm for Outlier Detection in WSNs

Content Provider	IEEE Xplore Digital Library
Author	Suya Xu Caiping Hu Lisong Wang Guobin Zhang
Copyright Year	2012
Abstract	Support vector machine approach is an effective technique to solve poly-dimensional outlier detection, which can avoid the curse of dimensionality problem and has higher accuracy. One-class support vector machine-based outlier detection techniques take advantage of spatial and temporal correlations that exist between sensor data to cooperatively identify outliers. However, for large scale training samples, SVM techniques take more spatial and temporal overhead to process and optimize training samples. In this paper, we propose KNN-SVM techniques (Support Vector Machines based on K-Nearest Neighbor Algorithm) for Outlier Detection in Wireless Sensor Networks. It utilizes KNN techniques to reduce training samples' scale which can shorten training time and optimize time. Then it maps the samples into feature space by kernel function. Experiments with data collected from the Intel Berkeley Research Laboratory show that our techniques are feasible and can effectively reduce spatial and temporal consumption with high accuracy.
Starting Page	1
Ending Page	4
File Size	276169
Page Count	4
File Format	PDF
ISBN	9781612846842
ISSN	21619654
e-ISBN	9781612846835
DOI	10.1109/WiCOM.2012.6478696
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Wireless sensor networks Support vector machine classification Vectors Data mining Kernel
Content Type	Text
Resource Type	Article

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