Weighted ensemble of gross error detection methods based on particle swarm optimization.

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

Abstract

Gross errors, a kind of non-random error caused by process disturbances or leaks, can make reconciled estimates can be very inaccurate and even infeasible. Detecting gross errors thus prevents financial loss from incorrectly accounting and also identifies potential environmental consequences because of leaking. In this study, we develop an ensemble of gross error detection (GED) methods to improve the effectiveness of the gross error identification on measurement data. We propose a weighted combining method on the outputs of all constituent GED methods and then compare the combined result to a threshold to conclude about the presence of the gross error. We generate a set of measurements with or without gross error and then minimize the GED error rate of the proposed ensemble on this set with respect to the combining weights and threshold. The Particle Swarm Optimization method is used to solve this optimization problem. Experiments conducted on a simulated system show that our ensemble is better than all constituent GED methods and two ensemble methods.

Citation

DOBOS, D., NGUYEN, T.T., MCCALL, J., WILSON, A., STOCKTON, P. and CORBETT, H. 2021. Weighted ensemble of gross error detection methods based on particle swarm optimization. In Chicano, F. (ed) Proceedings of the 2021 Genetic and evolutionary computation conference (GECCO 2021), 10-14 July 2021, [virtual conference]. New York: ACM [online], pages 307-308. Available from: https://doi.org/10.1145/3449726.3459415

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2021 Genetic and evolutionary computation conference (GECCO 2021)
• Start Date: Jul 10, 2021
• End Date: Jul 14, 2021
• Acceptance Date: Mar 26, 2021
• Online Publication Date: Jun 26, 2021
• Publication Date: Jun 30, 2021
• Deposit Date: Feb 2, 2023
• Publicly Available Date: Feb 2, 2023
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Pages: 307-308
• ISBN: 9781450383509
• DOI: https://doi.org/10.1145/3449726.3459415
• Keywords: Gross error detection; Ensemble method; Particle swarm optimization; Ensemble learning
• Public URL: https://rgu-repository.worktribe.com/output/1405777