A Construction Method of Fuzzy Classifiers Using Confidence-weighted Learning

Incremental algorithms for fuzzy classifiers are studied in this paper. It is assumed that not all training patterns are given a priori for training classifiers, but are gradually made available over time. It is also assumed that the previously available training patterns can not be used afterwards. Thus, fuzzy classifiers should be modified by updating already constructed classifiers using the available training patterns. In this paper, a confidence-weighted (CW) learning algorithm is applied to fuzzy classifiers for this task. A series of computational experiments are conducted in order to examine the performance of the proposed method comparing that method with the conventional learning algorithm for fuzzy classifiers. c © 2013 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of KES International.