Determining the Maximum Hyperbox Size in a Min-Max Fuzzy Classifier Using a Regression Model
DOI: 10.21293/1818-0442-2025-28-3-145-152
Abstract: The paper proposes an algorithm for constructing fuzzy Min-Max classifier with adaptation of the maximum hyperbox size parameter using a regression model. The parameter-estimation model was developed using machine learning methods. To enable this, a set of 38 meta-features was proposed to character-ize dataset properties; these features were computed recurrently to support online learning. A computational experiment was conducted to construct classifiers using the proposed algorithm for solving cybersecurity problems such as spam detection, phishing-website detection, and network-attack detection. In the spam- and phishing-detection tasks, the proposed algorithm demonstrated a statistically significant improvement in accuracy compared to the Min-Max classifier that does not employ a regression model.
Keywords: fuzzy Min-Max classifier, meta-features, regression models, incremental learning, cybersecurity, automatic parameter selec-tion
Funding: The study was supported by the Russian Science Foundation grant No. 24-21-00168, https://rscf.ru/project/24-21-00168/.
For citation:
Sarin K. S., Kolomnikov R. E., Hodashinskiy I. A. Determining the Maximum Hyperbox Size in a Min-Max Fuzzy Classifier Using a Regression Model. Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki, 2025, vol. 28, no. 3, pp. 145–152. DOI: 10.21293/1818-0442-2025-28-3-145-152
Authors and copyright holders:
- Sarin K. S. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)
- Kolomnikov R. E. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)
- Hodashinskiy I. A. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)
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