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Combined multiclass classification and anomaly detection for large-scale wireless sensor networks
conference contribution
posted on 2013-01-01, 00:00 authored by Alistair ShiltonAlistair Shilton, Sutharshan RajasegararSutharshan Rajasegarar, M PalaniswamiA smart wireless sensor network analytics requirement, beyond routine data collection, aggregation and analysis, in large-scale applications, is the automatic classification of emerging unknown events (classes) from the known classes. In this paper we present a new form of SVM that combines multiclass classification and anomaly detection into a single step to improve performance when data contains vectors from classes not represented in the training set. We demonstrate how the concepts of structural risk minimisation and anomaly detection are combined and analysing the effect of the various training parameters. The evaluations on several benchmark datasets reveal its ability to accurately classify unknown classes and known classes simultaneously.
History
Event
IEEE Sensors Council. Conference (8th : 2013 : Melbourne, Victoria)Series
IEEE Sensors Council ConferencePagination
491 - 496Publisher
Institute of Electrical and Electronics EngineersLocation
Melbourne, Vic.Place of publication
Piscataway, N.J.Publisher DOI
Start date
2013-04-02End date
2013-04-05ISBN-13
978-1-4673-5501-8Language
engPublication classification
E Conference publication; E1.1 Full written paper - refereedCopyright notice
2013, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
IEEE ISSNIP 2013 : Proceedings of the 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information ProcessingUsage metrics
Categories
Keywords
TrainingSupport vector machinesVectorsZincGlassKernelWireless sensor networksData analysisPattern classificationRisk analysisScience & TechnologyTechnologyComputer Science, Hardware & ArchitectureEngineering, Electrical & ElectronicRemote SensingComputer ScienceEngineeringSUPPORTInformation SystemsArtificial Intelligence and Image Processing
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