A weighted ensemble of regression methods for gross error identification problem.

Dobos, Daniel; Dang, Truong; Nguyen, Tien Thanh; McCall, John; Wilson, Allan; Corbett, Helen; Stockton, Phil

Abstract

In this study, we proposed a new ensemble method to predict the magnitude of gross errors (GEs) on measurement data obtained from the hydrocarbon and stream processing industries. Our proposed model consists of an ensemble of regressors (EoR) obtained by training different regression algorithms on the training data of measurements and their associated GEs. The predictions of the regressors are aggregated using a weighted combining method to obtain the final GE magnitude prediction. In order to search for optimal weights for combining, we modelled the search problem as an optimisation problem by minimising the difference between GE predictions and corresponding ground truths. We used Genetic Algorithm (GA) to search for the optimal weights associated with each regressor. The experiments were conducted on synthetic measurement data generated from 4 popular systems from the literature. We first conducted experiments in comparing the performances of the proposed ensemble using GA and Particle Swarm Optimisation (PSO), nature-based optimisation algorithms to search for combining weights to show the better performance of the proposed ensemble with GA. We then compared the performance of the proposed ensemble to those of two well-known weighted ensemble methods (Least Square and BEM) and two ensemble methods for regression problems (Random Forest and Gradient Boosting). The experimental results showed that although the proposed ensemble took higher computational time for the training process than those benchmark algorithms, it performed better than them on all experimental datasets.

Citation

DOBOS, D., DANG, T., NGUYEN, T.T., MCCALL, J., WILSON, A., CORBETT, H. and STOCKTON, P. 2023. A weighted ensemble of regression methods for gross error identification problem. In Proceedings of the 2023 IEEE (Institute of Electrical and Electronics Engineers) Symposium series on computational intelligence (SSCI 2023), 5-8 December 2023, Mexico City, Mexico. Piscataway: IEEE [online], pages 413-420. Available from: https://doi.org/10.1109/SSCI52147.2023.10371882

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2023 IEEE (Institute of Electrical and Electronics Engineers) Symposium series on computational intelligence (SSCI 2023)
• Start Date: Dec 5, 2023
• End Date: Dec 8, 2023
• Acceptance Date: Sep 15, 2023
• Online Publication Date: Dec 31, 2023
• Publication Date: Dec 31, 2023
• Deposit Date: Feb 1, 2024
• Publicly Available Date: Feb 1, 2024
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Pages: 413-420
• DOI: https://doi.org/10.1109/SSCI52147.2023.10371882
• Keywords: Gross error; Ensemble method; Regression; Genetic algorithm; Weighted ensemble
• Public URL: https://rgu-repository.worktribe.com/output/2225928