文献类型：期刊文献

英文题名：Comprehensive Outlier Detection in Wireless Sensor Network with Fast Optimization Algorithm of Classification Model

作者：Yao, Haiqing[1];Cao, Heng[1];Li, Jin[1]

机构：[1]E China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China

年份：2015

卷号：2015

外文期刊名：INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS

收录：;EI(收录号：20153101091369);WOS:【SCI-EXPANDED(收录号:WOS:000359214000001)】；

基金：This work was funded in part by the National Natural Science Foundation of China (Grant 51275170).

语种：英文

外文关键词：Data handling - Statistics - Computational complexity - Iterative methods - Optimization - Complex networks - Object detection

摘要：Since the nonstationary distribution of the detected objects is general in the real world, the accurate and efficient outlier detection for data analysis within wireless sensor network (WSN) is a challenge. Recently, with high classification precision and affordable complexity, one-class quarter-sphere support vector machine (QSSVM) has been introduced to deal with the online and adaptive outlier detection in WSN. Regarding the one-sided consideration of optimization or iterative updating algorithm for QSSVM model within current techniques, we have proposed comprehensive outlier detection methods in WSN based on the QSSVM algorithm. To reduce the complexity of optimization algorithm for QSSVM model in existing techniques, a fast optimization algorithm based on average Euclidean distance has been developed and employed to the comprehensive outlier detection method. Evaluated by real and synthetic WSN data sets, our methods have shown an excellent outlier detection performance, and they have been proved to meet the requirements of online adaptive outlier detection in the case of nonstationary detection tasks of WSN.