Fuzzy Genetics-Based Machine Learning to Handle Continually Increasing Unknown Classes

Abstract

The recent rapid development of information technology has enabled us to collect data continually. The accumulated data is regarded as an important economic resource. Many researchers have studied techniques for extracting knowledge in an interpretable format from continually increasing data in view of quality and quantity. Fuzzy genetics-based machine learning (GBML) is one of the most effective methods to design a classifier to extract interpretable knowledge. By applying fuzzy GBML, we can obtain a fuzzy classifier composed of linguistically interpretable rules. However, fuzzy GBML cannot learn in a situation where training data with unknown class labels increases continually (i.e., class incremental situation) because its learning algorithm is batch learning. In this paper, we propose class incremental fuzzy GBML (CI-FGBML), which can learn in the class incremental situation. Specifically, when patterns in an unknown class obtains, CI-FGBML performs two operations: i) regeneration of rules for classifying the unknown class, and ii) reduction of training data belonging to trained classes. Experimental results show that the proposed method can learn efficiently in the class incremental situation from the viewpoint of computational cost.