| Popis: |
To extract as much information as possible from the gas sensor responses of an electronic nose (E-Nose) system, feature extraction methods are adopted to obtain meaningful information of data. However, among a large quantity of the extracted features, only a few are actually informative for gas classification. Even worse, some of the features may degrade the accuracy of classification. To solve this problem, we propose a minimum distance inliers probability (MDIP) feature selection (FS) method that eliminates unnecessary features by considering their degree of clustering and degree of separation. The performance was validated using an open-access dataset. After applying the MDIP method, the number of features was reduced from 48 to 18 in average, while the average classification accuracy was improved from 46.45% to 80.6%, validating the efficiency of the MDIP method. |
