A Voting Ensemble Technique for Gas Classification

A spectrum of concepts relates to Artificial Intelligence, namely machine learning and committee machine learning. In the past few decades, they have been around and have frequently been implemented or considered for the use of automating tasks that mankind can do without specific instructions and guidelines. The focus of this paper is on gas classification and identification by using individual machine learning (Logistic Regression (LR), Naïve Bayes (NB)s, K-Nearest Neighbours (k-NN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF)) and ensemble (Stacking and Voting) techniques. Six different gases and a 4×4 sensor array is used for data collection. Using data collected by sensors arrays, it has been proven that our system is more accurate than individual classifiers. Improved accuracy of 98.04% is achieved by using Voting Classifier.