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Conference papers
Min-max hyperparameter tuning, with application to fault detection
Abstract : In order to reach satisfactory performance, fault diagnosis methods require the tuning of internal parameters, usually called hyperparameters. This is generally achieved by optimizing a performance criterion, typically a trade-off between false-alarm and non-detection rates. Perturbations should also be taken into account, for instance by considering the worst possible case. A new method to achieve such a tuning is described, which is especially interesting when the simulations required are so costly that their number is severely limited. It achieves min-max optimization of the tuning parameters via a relaxation procedure and Kriging-based optimization. This approach is applied to the worst-case optimal tuning of a fault diagnosis method consisting of an observer-based residual generator followed by a statistical test. It readily extends to the tuning of hyperparameters in other contexts.
Cited literature [19 references]
https://hal-supelec.archives-ouvertes.fr/hal-00615618
Contributor : Julien Marzat <>
Submitted on : Thursday, September 8, 2011 - 9:22:35 AM
Last modification on : Wednesday, September 16, 2020 - 4:42:22 PM
Long-term archiving on: : Friday, December 9, 2011 - 2:20:29 AM
Contributor : Julien Marzat <>
Submitted on : Thursday, September 8, 2011 - 9:22:35 AM
Last modification on : Wednesday, September 16, 2020 - 4:42:22 PM
Long-term archiving on: : Friday, December 9, 2011 - 2:20:29 AM
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IFAC2011_476.pdf
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