A Minimum Distance Inlier Probability (MDIP) Feature Selection Method to Improve Gas Classification for Electronic Nose Systems

Abstract

For electronic nose systems to obtain meaningful information from sensor data, sensor response features are first extracted for further signal processing. However, redundant features may diminish the accuracy of gas classification. To solve this problem, a minimum distance inlier probability (MDIP) feature selection (FS) method is proposed. By incorporating the intrinsic properties of features and ranking strategy, MDIP can efficiently eliminate redundant features and provide better classification accuracy. The performance of the method was validated on two open-access datasets that provide information for system variation and sensor drift problems, respectively. Experimental results revealed that the average classification accuracy for the two datasets was higher by 46.1% and 37.5%, respectively, with the MDIP method.