Sensor Drift Compensation in Time Series Prediction through Regularized Ensemble of Classifiers

Abstract

Concept drift is a major problem that the chemical sensor community is facing in their research and development. This arises when the predictive characteristic feature of the target variable in the sensor setup, changes due to some chemical or physical interaction of the environment elements with the surface of the sensor and other factors such as aging and poisoning of the surface. This is not a new problem but is having a wide scope of further developments. Recent research suggests an upcoming solution for this drift compensation as ensemble of classifiers with uniform weightage to all the participating classifiers or some non-uniform weightage according to performance of individual classifier. There arise some ill posed problems with this ensemble of classifiers. This paper introduces a new machine learning approach to solve this problem through regularized weighted ensemble of classifiers for overcoming the time dependent drift occurrence. The weights are chosen by regularized majority voting which are then associated with the individual classifiers to form the ensembles. This regularized drift compensation algorithm is applied to solve the gas discrimination problem for classification of 6 different volatile organic compounds over time series data set consisting of data recorded using an array of 16 metal oxides sensors for 3 years. Our experiment tries various different regularization approaches and finds best improvement in case of double regularization of Single Value Decomposition and L2 regularization. Results clearly indicate improved classification accuracy in presence of drift and better results than recent reporting. To the best of our knowledge, such a machine learning approach has not yet been applied for drift correction in sensor community.

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