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https://doi.org/10.21256/zhaw-23849| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | A model-agnostic algorithm for Bayes error determination in binary classification |
| Autor/-in: | Michelucci, Umberto Sperti, Michela Piga, Dario Venturini, Francesca Deriu, Marco A. |
| et. al: | No |
| DOI: | 10.3390/a14110301 10.21256/zhaw-23849 |
| Erschienen in: | Algorithms |
| Band(Heft): | 14 |
| Heft: | 11 |
| Seite(n): | 301 |
| Erscheinungsdatum: | Okt-2021 |
| Verlag / Hrsg. Institution: | MDPI |
| ISSN: | 1999-4893 |
| Sprache: | Englisch |
| Schlagwörter: | Machine learning; Intrinsic limit; ROC curve; Binary classification; Naïve Bayes classifier |
| Fachgebiet (DDC): | 510: Mathematik |
| Zusammenfassung: | 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 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY 4.0: Namensnennung 4.0 International |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Angewandte Mathematik und Physik (IAMP) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2021_Michelucci-etal_Model-agnostic-algorithm-Bayes-error-determination.pdf | 515.65 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
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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