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Multiple classifier system for automated quality assessment of marine sensor data
Citation
Rahman, A and Smith, DV and Timms, GP, Multiple classifier system for automated quality assessment of marine sensor data, Proceedings of the 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 2-5 April 2013, Melbourne, Australia, pp. 362-367. ISBN 978-1-4673-5500-1 (2013) [Refereed Conference Paper]
Copyright Statement
Copyright 2013 IEEE
Official URL: http://ieeexplore.ieee.org/document/6529817/
DOI: doi:10.1109/ISSNIP.2013.6529817
Abstract
Numerous sources of uncertainty are associated with the data acquisition process in marine sensor networks. It is thus required to assure that the data quality of sensors is fit for the intended purpose. We propose a supervised learning framework to infer the quality of sensor observations online. A problem with using supervised classification in quality assessment is that sensor observations from the class of uncertain data will be far out-weighed by class instances of good data quality. This leads to an imbalanced data set, which can potentially reduce the classification accuracy of uncertain data. A multiple classifier (or ensemble classifier) system is proposed to deal with this problem. Training sets are randomly undersampled to develop training subsets with balanced class membership. The process is repeated to produce multiple balanced training subsets. Individual classifiers are then trained upon each of these balanced data sets. The quality classifications from the individual classifiers are then combined using majority voting. We evaluated the ensemble classifier system using conductivity and temperature sensors from the Tasmanian Marine Analysis Network (TasMAN). Experiments demonstrate that the ensemble classifier balances the classification accuracy of the majority and minority classes, achieving a higher overall classification accuracy than its constituent classifiers.
Item Details
Item Type: | Refereed Conference Paper |
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Keywords: | data quality assessment, multiple classifier system |
Research Division: | Information and Computing Sciences |
Research Group: | Computer vision and multimedia computation |
Research Field: | Pattern recognition |
Objective Division: | Information and Communication Services |
Objective Group: | Information systems, technologies and services |
Objective Field: | Information systems, technologies and services not elsewhere classified |
UTAS Author: | Timms, GP (Dr Gregory Timms) |
ID Code: | 116703 |
Year Published: | 2013 |
Deposited By: | Engineering |
Deposited On: | 2017-05-17 |
Last Modified: | 2017-06-21 |
Downloads: | 0 |
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