Noise Models in Classification: Unified Nomenclature, Extended Taxonomy and Pragmatic Categorization
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AuthorSáez Muñoz, José Antonio
Noise modelsNomenclatureTaxonomyNoisy dataClassification
Sáez, J.A. Noise Models in Classification: Unified Nomenclature, Extended Taxonomy and Pragmatic Categorization. Mathematics 2022, 10, 3736. [https://doi.org/10.3390/math10203736]
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.