Noise Models in Classification: Unified Nomenclature, Extended Taxonomy and Pragmatic Categorization

José A. Sáez ()
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José A. Sáez: Department of Statistics and Operations Research, University of Granada, Fuente Nueva s/n, 18071 Granada, Spain

Mathematics, 2022, vol. 10, issue 20, 1-20

Abstract: 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.

Keywords: noise models; nomenclature; taxonomy; noisy data; classification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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