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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

Authors

Allan Wilson

Phil Stockton

Helen Corbett



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

Conference Name 2021 Genetic and evolutionary computation conference (GECCO 2021)
Conference Location [virtual conference]
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)
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

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