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Multiple classifier system for automated quality assessment of marine sensor data
conference contribution
posted on 2023-05-23, 12:00 authored by Rahman, A, Smith, DV, Timms, GPNumerous 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.
History
Publication title
Proceedings of the 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information ProcessingEditors
M Palaniswami, C Leckie, S Kanhere, J GubbiPagination
362-367ISBN
978-1-4673-5500-1Publisher
IEEEPlace of publication
Piscataway, United StatesEvent title
IEEE ISSNIP: Sensing the FutureEvent Venue
Melbourne, AustraliaDate of Event (Start Date)
2013-04-02Date of Event (End Date)
2013-04-05Rights statement
Copyright 2013 IEEERepository Status
- Restricted