A Study on the linguistic explanation of online classification mechanism using Confidence-Weighted learning fuzzy classifiers

Abstract

Fuzzy classifiers can linguistically explain its classification mechanism while achieving high classification accuracy. In this paper, we aim to explain the classification mechanism in dynamic environments where the classification boundary changes over time. For this purpose, a fuzzy classifier is constructed in an online manner by means of Confidence-Weighted learning. Online learning allows the classifier to be trained from a small number of training patterns. We have confirmed that the learning model can linguistically explain the classification mechanism by examining how the weights of the fuzzy if-then rules in the fuzzy classifier so that the fuzzy classifier dynamically follows the changes in the classification boundary.