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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-28559
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dc.contributor.authorMichelucci, Umberto-
dc.contributor.authorVenturini, Francesca-
dc.date.accessioned2023-09-01T13:01:49Z-
dc.date.available2023-09-01T13:01:49Z-
dc.date.issued2023-08-
dc.identifier.issn0957-4174de_CH
dc.identifier.issn1873-6793de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/28559-
dc.description.abstractThe application of machine learning to physics problems is widely found in the scientific literature. Both regression and classification problems are addressed by a large array of techniques that involve learning algorithms. Unfortunately, the measurement errors of the data used to train machine learning models are almost always neglected. This leads to estimations of the performance of the models (and thus their generalization power) that is too optimistic since it is always assumed that the target variables (what one wants to predict) are correct. In physics, this is a dramatic deficiency as it can lead to the belief that theories or patterns exist where, in reality, they do not. This paper addresses this deficiency by deriving formulas for commonly used metrics (both for regression and classification problems) that take into account measurement errors of target variables. The new formulas give an estimation of the metrics which is always more pessimistic than what is obtained with the classical ones, not taking into account measurement errors. The formulas given here are of general validity, completely model-independent, and can be applied without limitations. Thus, with statistical confidence, one can analyse the existence of relationships when dealing with measurements with errors of any kind. The formulas have wide applicability outside physics and can be used in all problems where measurement errors are relevant to the conclusions of studies.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofExpert Systems with Applicationsde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectMachine learningde_CH
dc.subjectMetricsde_CH
dc.subjectPhysicsde_CH
dc.subjectComputational physicsde_CH
dc.subjectMeasurement errorde_CH
dc.subjectError propagationde_CH
dc.subjectRegressionde_CH
dc.subjectClassificationde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc530: Physikde_CH
dc.titleNew metric formulas that include measurement errors in machine learning for natural sciencesde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Mathematik und Physik (IAMP)de_CH
dc.identifier.doi10.1016/j.eswa.2023.120013de_CH
dc.identifier.doi10.21256/zhaw-28559-
zhaw.funding.euNode_CH
zhaw.issue120013de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume224de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedPhotonicsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Michelucci, U., & Venturini, F. (2023). New metric formulas that include measurement errors in machine learning for natural sciences. Expert Systems with Applications, 224(120013). https://doi.org/10.1016/j.eswa.2023.120013
Michelucci, U. and Venturini, F. (2023) ‘New metric formulas that include measurement errors in machine learning for natural sciences’, Expert Systems with Applications, 224(120013). Available at: https://doi.org/10.1016/j.eswa.2023.120013.
U. Michelucci and F. Venturini, “New metric formulas that include measurement errors in machine learning for natural sciences,” Expert Systems with Applications, vol. 224, no. 120013, Aug. 2023, doi: 10.1016/j.eswa.2023.120013.
MICHELUCCI, Umberto und Francesca VENTURINI, 2023. New metric formulas that include measurement errors in machine learning for natural sciences. Expert Systems with Applications. August 2023. Bd. 224, Nr. 120013. DOI 10.1016/j.eswa.2023.120013
Michelucci, Umberto, and Francesca Venturini. 2023. “New Metric Formulas That Include Measurement Errors in Machine Learning for Natural Sciences.” Expert Systems with Applications 224 (120013). https://doi.org/10.1016/j.eswa.2023.120013.
Michelucci, Umberto, and Francesca Venturini. “New Metric Formulas That Include Measurement Errors in Machine Learning for Natural Sciences.” Expert Systems with Applications, vol. 224, no. 120013, Aug. 2023, https://doi.org/10.1016/j.eswa.2023.120013.


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