Gas chemical sensors are strongly affected by drift, i.e., changes in sensors’ response with time, that may turn statistical models commonly used for classification completely useless after a period of time. This paper presents a new classifier that embeds an adaptive stage able to reduce drift effects. The proposed system exploits a state-of-the-art evolutionary strategy to iteratively tweak the coefficients of a linear transformation able to transparently transform raw measures in order to mitigate the negative effects of the drift. The system operates continuously. The optimal correction strategy is learnt without a-priori models or other hypothesis on the behavior of physical-chemical sensors. Experimental results demonstrate the efficacy of the approach on a real problem.
Exploiting Evolution for an Adaptive Drift-Robust Classifier in Chemical Sensing / DI CARLO, Stefano; Falasconi, M.; SANCHEZ SANCHEZ, EDGAR ERNESTO; Scionti, A.; Squillero, Giovanni; Tonda, A.. - STAMPA. - 6024/2010:(2010), pp. 412-421. (Intervento presentato al convegno EvoApplicatons 2010: EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC tenutosi a Istanbul, Turkey, nel April 7-9, 2010) [10.1007/978-3-642-12239-2_43].
Exploiting Evolution for an Adaptive Drift-Robust Classifier in Chemical Sensing
DI CARLO, STEFANO;SANCHEZ SANCHEZ, EDGAR ERNESTO;SQUILLERO, Giovanni;
2010
Abstract
Gas chemical sensors are strongly affected by drift, i.e., changes in sensors’ response with time, that may turn statistical models commonly used for classification completely useless after a period of time. This paper presents a new classifier that embeds an adaptive stage able to reduce drift effects. The proposed system exploits a state-of-the-art evolutionary strategy to iteratively tweak the coefficients of a linear transformation able to transparently transform raw measures in order to mitigate the negative effects of the drift. The system operates continuously. The optimal correction strategy is learnt without a-priori models or other hypothesis on the behavior of physical-chemical sensors. Experimental results demonstrate the efficacy of the approach on a real problem.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2363833
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