Machine Learning for Explosive Detection from Electronic Nose Datasets

Abstract

An electronic nose has been applied in many areas such as the food industry, the environmental area, and this technology can be used to detect some explosives. Many classification machine learning techniques are applied for creating the model which manipulates data into a defined group to be used for customer grouping, marketing, anomaly detection, and medical analysis. The purpose of this research is to find a suitable classification technique to be applied in an electronic nose to imitate the ability of sniffer dogs to detect the chemical substances. This research compares the accuracy of eight different classification techniques, which are Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest (RF), Adaptive Boosting, K-Nearest Neighbors, Gaussian Naive Bayes, and Multilayer Perceptron in both binary and multi-class gas sensor array open source datasets. Experimental results show the top algorithms are RF, and SVM models, which give average score as 99.66 and 98.93, respectively.