A Study on Multi-Class Online Fuzzy Classifiers for Dynamic Environments
This work addresses multi-class classification in dynamic settings for applications requiring real-time adaptation, but it is incremental as it builds on existing two-class methods.
The paper tackles the problem of extending conventional two-class online fuzzy classifiers to multi-class problems in dynamic environments, achieving performance evaluated through numerical experiments on synthetic and benchmark datasets.
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.