Online Active Learning with Drifted Data Streams Using Paired Ensemble Framework

¹ Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, 410073 Changsha, China
² College of Atmospheric Sciences, Lanzhou University, 730107 Lanzhou, China
³ Sino-Dutch Biomedical and Information Engineering School of Northeastern University, 110819 Shenyang, China

Abstract

In learning to classify data streams, it is impractical and expensive to label all of the instances. Online active learning over streaming data poses additional challenges for its increasing volumes and concept drifts. We propose a new online paired ensemble active learning framework consisting of a stable classifier and a timely substituted dynamic classifier to react to different types of concept drifts. Classifiers are built in block based way and will learn new instances incrementally online. According to a combination strategy of uncertainty strategy and random strategy, the decision whether to label the incoming instance for the updating of the stable classifier and the dynamic classifier will be made. Experimental evaluation results on real datasets show the advantage of the proposed work in comparison with other approaches.