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

TL;DR

This paper investigates the extension to the conventional fuzzy classifiers for multi-class problems and evaluates the performance of the multi-class online fuzzy classifiers through numerical experiments on synthetic dynamic data and also several benchmark datasets.

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.

Figures (9)

• Figure 1: Triangular membership functions.
• Figure 2: Generated four fuzzy if-then rules in the example.
• Figure 3: Generated boundary by the example fuzzy classifier.
• Figure 4: Don't Care membership function.
• Figure 5: Distributions of petal length and petal width of the iris dataset.