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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-23849
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dc.contributor.authorMichelucci, Umberto-
dc.contributor.authorSperti, Michela-
dc.contributor.authorPiga, Dario-
dc.contributor.authorVenturini, Francesca-
dc.contributor.authorDeriu, Marco A.-
dc.date.accessioned2022-01-07T11:55:09Z-
dc.date.available2022-01-07T11:55:09Z-
dc.date.issued2021-10-
dc.identifier.issn1999-4893de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/23849-
dc.description.abstractThis paper presents the intrinsic limit determination algorithm (ILD Algorithm), a novel technique to determine the best possible performance, measured in terms of the AUC (area under the ROC curve) and accuracy, that can be obtained from a specific dataset in a binary classification problem with categorical features regardless of the model used. This limit, namely, the Bayes error, is completely independent of any model used and describes an intrinsic property of the dataset. The ILD algorithm thus provides important information regarding the prediction limits of any binary classification algorithm when applied to the considered dataset. In this paper, the algorithm is described in detail, its entire mathematical framework is presented and the pseudocode is given to facilitate its implementation. Finally, an example with a real dataset is given.de_CH
dc.language.isoende_CH
dc.publisherMDPIde_CH
dc.relation.ispartofAlgorithmsde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectMachine learningde_CH
dc.subjectIntrinsic limitde_CH
dc.subjectROC curvede_CH
dc.subjectBinary classificationde_CH
dc.subjectNaïve Bayes classifierde_CH
dc.subject.ddc510: Mathematikde_CH
dc.titleA model-agnostic algorithm for Bayes error determination in binary classificationde_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.doi10.3390/a14110301de_CH
dc.identifier.doi10.21256/zhaw-23849-
zhaw.funding.euNode_CH
zhaw.issue11de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.start301de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume14de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Michelucci, U., Sperti, M., Piga, D., Venturini, F., & Deriu, M. A. (2021). A model-agnostic algorithm for Bayes error determination in binary classification. Algorithms, 14(11), 301. https://doi.org/10.3390/a14110301
Michelucci, U. et al. (2021) ‘A model-agnostic algorithm for Bayes error determination in binary classification’, Algorithms, 14(11), p. 301. Available at: https://doi.org/10.3390/a14110301.
U. Michelucci, M. Sperti, D. Piga, F. Venturini, and M. A. Deriu, “A model-agnostic algorithm for Bayes error determination in binary classification,” Algorithms, vol. 14, no. 11, p. 301, Oct. 2021, doi: 10.3390/a14110301.
MICHELUCCI, Umberto, Michela SPERTI, Dario PIGA, Francesca VENTURINI und Marco A. DERIU, 2021. A model-agnostic algorithm for Bayes error determination in binary classification. Algorithms. Oktober 2021. Bd. 14, Nr. 11, S. 301. DOI 10.3390/a14110301
Michelucci, Umberto, Michela Sperti, Dario Piga, Francesca Venturini, and Marco A. Deriu. 2021. “A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification.” Algorithms 14 (11): 301. https://doi.org/10.3390/a14110301.
Michelucci, Umberto, et al. “A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification.” Algorithms, vol. 14, no. 11, Oct. 2021, p. 301, https://doi.org/10.3390/a14110301.


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