Interfered feature elimination coupled with feature group selection for wound infection detection by electronic nose

Abstract

As the precise odor-sensing equipment, the electronic nose integrates multiple advanced and sensitive sensors that can identify wound infections non-invasively and rapidly by analyzing wound characteristic odor. To reduce the cost of sensors and improve or maintain e-nose’s performance, efficient optimization of sensor arrays is required. For this issue, we proposed a new sensor array optimization algorithm named Interfered Feature Elimination coupled with Feature Group Selection (IFE-FGS). In this method, the IFE algorithm first removed the bad sensor features; then the FGS algorithm determined the optimized sensor combination by gradually selecting the features in groups. The experimental results show the superiority of the IFE- FGS method on two bacteria datasets and six public gene expression profiling datasets. IFE- FGS achieves the classification accuracy of 93.95% and 94.94% in mean accuracy and max accuracy, respectively, on the bacteria dataset 2, which is significantly ahead of the comparison methods. Besides, our proposed method shows consistency and effectiveness. It achieves excellent performance, which takes two first-places, four second-places in mean accuracy, one first-place, and six second-places in max accuracy. Moreover, it also explores three novel and valuable discoveries for the electronic nose: 1) It can effectively identify biomarkers in the application. 2) It can effectively distinguish the degree of chemical components contributing to odors. 3) It can reveal the effective detection range of the targets.