A Study of Fuzzy Genetics-based Machine Learning to Class Incremental Problems

Abstract

This paper discusses a class incremental problem based on Fuzzy Genetics-based Machine Learning (GBML). In general, a learning algorithm of Fuzzy GBML is designed as a batch learning. Therefore, it requires a re-learning with a whole data if the Fuzzy GBML attempts to learn a new class data. We propose a method to efficiently generate rules corresponding to new class by selecting and relearn less importance rules out of rules classifying existing classes. From experimental results, the proposed method is able to perform the class incremental learning efficiently.