Manuscript received January 20, 2023; revised February 20, 2023; accepted March 24, 2023; published April 15, 2023
Abstract—This research aims at investigating performance of the ensemble learning method. The ensemble learning brings together various weak learners to create strong learners. Based on this ensemble learning idea, we develop a model for an efficient smoke detection tool. The three schemes of ensemble learning are investigated including bagging, boosting, and stacking. The bagging ensemble algorithm studied in this research is Random Forest and the boosting algorithm is AdaBoost. The stacking ensemble adopts three algorithms, that are Random Forest, AdaBoost, and Logistic Regression. The other learning algorithms adopted for performance comparison include Support Vector Machine, Naïve Bayes, and Decision Tree. The smoke detection data contain 62,630 records and 15 features. The dataset has been separated into training set and test set with a ratio of 75:25. The experimental results reveal that AdaBoost outperforms other learning algorithms when applied to the specific smoke detection application domain.
Index Terms—Smoke detection, ensemble learning, weak learner, bagging, boosting, stacking
P. Teerarassamee, K. Kerdprasop and N. Kerdprasop are with the School of Computer Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand.
R. Chanklan is with the Faculty of Engineering and Technology, School of Computer Engineering, Rajamangala University of Technology Isan, Nakhon Ratchasima, Thailand.
*Correspondence: psk.trrsm@gmail.com (P.T.)
Cite: Pongsakorn Teerarassamee, Ratiporn Chanklan, Kittisak Kerdprasop, and Nittaya Kerdprasop, "Smoke Detection with Ensemble Modeling," International Journal of Modeling and Optimization vol. 13, no. 2, pp. 44-47, 2023.
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