A model-agnostic algorithm for Bayes error determination in binary classification

Michelucci, Umberto; Sperti, Michela; Piga, Dario; Venturini, Francesca; Deriu, Marco A.

doi:10.3390/a14110301

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-23849

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	A model-agnostic algorithm for Bayes error determination in binary classification
Authors:	Michelucci, Umberto Sperti, Michela Piga, Dario Venturini, Francesca Deriu, Marco A.
et. al:	No
DOI:	10.3390/a14110301 10.21256/zhaw-23849
Published in:	Algorithms
Volume(Issue):	14
Issue:	11
Page(s):	301
Issue Date:	Oct-2021
Publisher / Ed. Institution:	MDPI
ISSN:	1999-4893
Language:	English
Subjects:	Machine learning; Intrinsic limit; ROC curve; Binary classification; Naïve Bayes classifier
Subject (DDC):	510: Mathematics
Abstract:	This 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.
URI:	https://digitalcollection.zhaw.ch/handle/11475/23849
Fulltext version:	Published version
License (according to publishing contract):	CC BY 4.0: Attribution 4.0 International
Departement:	School of Engineering
Organisational Unit:	Institute of Applied Mathematics and Physics (IAMP)
Appears in collections:	Publikationen School of Engineering

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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.