Fuzzy Min-Max Сlassifier: Review
DOI: 10.21293/1818-0442-2023-26-1-65-75
Abstract: Online adaptation and interpretability have become one of the important requirements for machine learning models. Popular models such as artificial neural networks cannot fully implement them. Fuzzy classifiers of the Min-Max type are interpretable, thanks to the underlying fuzzy logic theory, and adaptable with the advent of new information. This article presents a comprehensive literature review on machine learning models based on fuzzy Min-Max classifiers. The architecture of the classifier and the principle of its operation are presented. A review of the modifications of the classifier is carried out and their effectiveness is evaluated. Applications of the classifier and its modifications in solving real problems are indicated. In conclusion, statements are drawn about the work of the classifier and the problems that have remained unresolved.
Keywords: machine learning, fuzzy classifier, data analysis, classifier adaptation
Funding: The work was carried out with financial support from the Russian Science Foundation (RSF), grant No. 22-21-00021.
For citation:
Sarin K. S. Fuzzy Min-Max Сlassifier: Review. Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki, 2023, vol. 26, no. 1, pp. 65–75. DOI: 10.21293/1818-0442-2023-26-1-65-75
Authors and copyright holders:
- Sarin K. S. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)
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