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[2602.14375] A Study on Multi-Class Online Fuzzy Classifiers for Dynamic Environments
Summary
This paper presents a multi-class online fuzzy classifier designed for dynamic environments, extending traditional two-class fuzzy classifiers to handle multiple classes effectively.
Why It Matters
As machine learning applications increasingly operate in dynamic environments, this study addresses a critical gap in existing fuzzy classification methods. By enabling multi-class classification in real-time scenarios, it enhances the adaptability and performance of AI systems, making it relevant for researchers and practitioners in machine learning and AI.
Key Takeaways
- Introduces a multi-class online fuzzy classifier for dynamic environments.
- Extends conventional fuzzy classifiers, which typically handle only two-class problems.
- Evaluates performance using synthetic dynamic data and benchmark datasets.
- Addresses the need for adaptable AI solutions in real-time applications.
- Contributes to the ongoing research in fuzzy logic and machine learning.
Computer Science > Machine Learning arXiv:2602.14375 (cs) [Submitted on 16 Feb 2026] Title:A Study on Multi-Class Online Fuzzy Classifiers for Dynamic Environments Authors:Kensuke Ajimoto, Yuma Yamamoto, Yoshifumi Kusunoki, Tomoharu Nakashima View a PDF of the paper titled A Study on Multi-Class Online Fuzzy Classifiers for Dynamic Environments, by Kensuke Ajimoto and 3 other authors View PDF HTML (experimental) Abstract:This paper proposes a multi-class online fuzzy classifier for dynamic environments. A fuzzy classifier comprises a set of fuzzy if-then rules where human users determine the antecedent fuzzy sets beforehand. In contrast, the consequent real values are determined by learning from training data. In an online framework, not all training dataset patterns are available beforehand. Instead, only a few patterns are available at a time step, and the subsequent patterns become available at the following time steps. The conventional online fuzzy classifier considered only two-class problems. This paper investigates the extension to the conventional fuzzy classifiers for multi-class problems. We evaluate the performance of the multi-class online fuzzy classifiers through numerical experiments on synthetic dynamic data and also several benchmark datasets. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2602.14375 [cs.LG] (or arXiv:2602.14375v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.14375 Focus to learn more arXiv-issued DOI via DataCite (p...
