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Metal Oxide Gas Sensor Drift Compensation Using a Dynamic Classifier Ensemble Based on Fitting
College of Electronic Science and Technology, Dalian University of Technology, No.2 Linggong Road, Dalian 116024, China
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Received: 18 April 2013; in revised form: 11 July 2013 / Accepted: 15 July 2013 / Published: 17 July 2013
Abstract: Sensor drift is currently the most challenging problem in gas sensing. We propose a novel ensemble method with dynamic weights based on fitting (DWF) to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. The DWF method uses a dynamic weighted combination of support vector machine (SVM) classifiers trained by the datasets that are collected at different time periods. In the testing of future datasets, the classifier weights are predicted by fitting functions, which are obtained by the proper fitting of the optimal weights during training. We compare the performance of the DWF method with that of competing methods in an experiment based on a public dataset that was compiled over a period of three years. The experimental results demonstrate that the DWF method outperforms the other methods considered. Furthermore, the DWF method can be further optimized by applying a fitting function that more closely matches the variation of the optimal weight over time.
Keywords: sensor drift; metal oxide sensors; ensemble method; dynamic weights
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MDPI and ACS Style
Liu, H.; Tang, Z. Metal Oxide Gas Sensor Drift Compensation Using a Dynamic Classifier Ensemble Based on Fitting. Sensors 2013, 13, 9160-9173.
Liu H, Tang Z. Metal Oxide Gas Sensor Drift Compensation Using a Dynamic Classifier Ensemble Based on Fitting. Sensors. 2013; 13(7):9160-9173.
Liu, Hang; Tang, Zhenan. 2013. "Metal Oxide Gas Sensor Drift Compensation Using a Dynamic Classifier Ensemble Based on Fitting." Sensors 13, no. 7: 9160-9173.