A Study on Multi-Class Online Fuzzy Classifiers for Dynamic Environments

TL;DR

This paper introduces a multi-class online fuzzy classifier designed for dynamic environments, extending traditional two-class models, and evaluates its performance on synthetic and benchmark datasets.

Contribution

It presents a novel multi-class online fuzzy classification method tailored for dynamic data streams, addressing a gap in existing two-class focused models.

Findings

Effective handling of multi-class problems in dynamic environments

Demonstrated superior performance on synthetic and benchmark datasets

Adaptability to evolving data streams

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.