Heuristic random forests (HRF) for drift compensation in electronic nose applications

Electronic nose is an instrument equipped with chemical gas sensors and is used to sense, identify, and measure different odors. The problem arises when these sensors incorporate drift by the passage of time. The effect of drift is so adverse that the pattern recognition algorithms used for identification and measurement of odors fail to respond accurately. To overcome the challenge of drift in sensors, one of the most widely used techniques is system re-calibration, which is a cumbersome process. Keeping in mind the challenges of drift and issues of system re-calibration for real-life applications, this paper proposes a novel method to compensate drift in gas sensors with the following contributions: 1) the fitness function of a recursive metaheuristic optimization method is modified by embedding random forests learning for the quantification of six different gases under drift; 2) the proposed approach is able to compensate the long-term sensors drift without requiring any system re-calibration; and 3) the feature vector exploitation using particle swarm optimization reduces the computational complexity and increases the prediction accuracy of the system. A comparison is provided with different state-of-the-art approaches, and the proposed approach is found better in terms of prediction accuracy when tested on a benchmark dataset publically available.