A Voting Ensemble Technique for Gas Classification

Date

2022-07-14

Advisors

Journal Title

Journal ISSN

ISSN

2367-3370

Volume Title

Publisher

Springer

Type

Conference

Peer reviewed

Yes

Abstract

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.

Description

The file attached to this record is the author's final peer reviewed version.

Keywords

Artificial Intelligence, Committee Machine Learning, Ensemble Learning, Voting Classifier, Stacking Classifier, Sensor Array, Classification

Citation

Jaleel, M., Malekmohamadi, H., Amira, A. (2022) A Voting Ensemble Technique for Gas Classification. In: Arai, K. (Ed.) Intelligent Computing. Cham: Springer, Proceedings of Computing Conference, London, July 2022.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)