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 Type:	Refereed Conference Paper
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