Optimization of Extracted Features from an Explosive-Detecting Electronic Nose Using Genetic Algorithm

The use of an electronic nose in detecting explosives has gained attention among researchers. This paper aims to optimize the extraction of features generated from a predetermined explosive-detecting electronic nose setup by using a genetic algorithm. A genetic algorithm (GA) is used to minimize the errors such as the mean error within explosive types, the mean error between explosive types and the classification error. The GA optimization program is implemented for each feature extraction technique, namely, principal component analysis (PCA) and linear discriminant analysis (LDA). As a result, the proponents were able to optimize the extracted features into a single point that can truly classify each explosive type. PCA is more preferred than LDA for practical purposes.