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

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  • José A. Sáez

    (Department of Statistics and Operations Research, University of Granada, Fuente Nueva s/n, 18071 Granada, Spain)

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.

Suggested Citation

  • José A. Sáez, 2022. "Noise Models in Classification: Unified Nomenclature, Extended Taxonomy and Pragmatic Categorization," Mathematics, MDPI, vol. 10(20), pages 1-20, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3736-:d:939000
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    References listed on IDEAS

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    1. Huang, Xiaolin & Shi, Lei & Suykens, Johan A.K., 2014. "Asymmetric least squares support vector machine classifiers," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 395-405.
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