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Evolved ensemble of detectors for gross error detection.

Nguyen, Tien Thanh ; McCall, John; Wilson, Allan; Ochei, Laud; Corbett, Helen; Stockton, Phil

Authors

Allan Wilson

Laud Ochei

Helen Corbett

Phil Stockton



Abstract

In this study, we evolve an ensemble of detectors to check the presence of gross systematic errors on measurement data. We use the Fisher method to combine the output of different detectors and then test the hypothesis about the presence of gross errors based on the combined value. We further develop a detector selection approach in which a subset of detectors is selected for each sample. The selection is conducted by comparing the output of each detector to its associated selection threshold. The thresholds are obtained by minimizing the 0-1 loss function on training data using the Particle Swarm Optimization method. Experiments conducted on a simulated system confirm the advantages of ensemble and evolved ensemble approach.

Citation

NGUYEN, T.T., MCCALL, J., WILSON, A., OCHEI, L., CORBETT, H. and STOCKTON, P. 2020. Evolved ensemble of detectors for gross error detection. In GECCO '20: proceedings of the Genetic and evolutionary computation conference companion (GECCO 2020), 8-12 July 2020, CancĂșn, Mexico. New York: ACM [online], pages 281-282. Available from: https://doi.org/10.1145/3377929.3389906

Conference Name 2020 Genetic and evolutionary computation conference (GECCO 2020)
Conference Location CancĂșn, Mexico
Start Date Jul 8, 2020
End Date Jul 12, 2020
Acceptance Date Mar 20, 2020
Online Publication Date Jul 8, 2020
Publication Date Jul 31, 2020
Deposit Date May 15, 2020
Publicly Available Date May 15, 2020
Publisher Association for Computing Machinery
Pages 281-282
Book Title GECCO '20: proceedings of the 2020 Genetic and evolutionary computation conference companion (GECCO 2020), 8-12 July 2020, Cancun, Mexico
ISBN 9781450371278
DOI https://doi.org/10.1145/3377929.3389906
Keywords Gross error detection; Ensemble method; Particle swarm optimization
Public URL https://rgu-repository.worktribe.com/output/905899

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