Time horizon selection using feature feedback for the implementation of an E-nose system

Several aspects of sensors should be considered for their practical application in reducing the cost and improving the performance of the system. Some factors, such as the power consumption, sampling period, processing time and memory size, are particularly important for efficient portable systems. In this paper, we propose a time horizon selection method for a portable sensor system. By using the feature feedback to investigate the relation between the input space and feature space, we find the distribution of discriminant information in a data sample and distinguish the time horizon with which we can improve the performance of the classification. The experimental results on different volatile organic compounds show that the proposed method provides for the good clustering of the different classes and increases classification rates from 95.3% to 96.9% and 98.2% for the selected time horizons, respectively.