Noise Models in Classification: Unified Nomenclature, Extended Taxonomy and Pragmatic Categorization Sáez Muñoz, José Antonio Noise models Nomenclature Taxonomy Noisy data Classification This paper presents the first review of noise models in classification covering both label and attribute noise. Their study reveals the lack of a unified nomenclature in this field. In order to address this problem, a tripartite nomenclature based on the structural analysis of existing noise models is proposed. Additionally, a revision of their current taxonomies is carried out, which are combined and updated to better reflect the nature of any model. Finally, a categorization of noise models is proposed from a practical point of view depending on the characteristics of noise and the study purpose. These contributions provide a variety of models to introduce noise, their characteristics according to the proposed taxonomy and a unified way of naming them, which will facilitate their identification and study, as well as the reproducibility of future research. 2022-11-29T11:50:52Z 2022-11-29T11:50:52Z 2022-10-11 info:eu-repo/semantics/article Sáez, J.A. Noise Models in Classification: Unified Nomenclature, Extended Taxonomy and Pragmatic Categorization. Mathematics 2022, 10, 3736. [https://doi.org/10.3390/math10203736] https://hdl.handle.net/10481/78172 10.3390/math10203736 eng http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional MDPI