Please use this identifier to cite or link to this item:
|Publication type:||Article in scientific journal|
|Type of review:||Peer review (publication)|
|Title:||A model-agnostic algorithm for Bayes error determination in binary classification|
Deriu, Marco A.
|Publisher / Ed. Institution:||MDPI|
|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.|
|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|
Files in This Item:
|2021_Michelucci-etal_Model-agnostic-algorithm-Bayes-error-determination.pdf||515.65 kB||Adobe PDF|
Show full item record
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, 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.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.