Combination of Active and Random Labeling Strategy in the Non-stationary Data Stream Classification

Abstract

A significant problem when building classifiers based on data stream is information about the correct label. Most algorithms assume access to this information without any restrictions. Unfortunately, this is not possible in practice because the objects can come very quickly and labeling all of them is impossible, or we have to pay for providing the correct label (e.g., to human expert). Hence, methods based on partially labeled data, including methods based on an active learning approach, are becoming increasingly popular, i.e., when the learning algorithm itself decides which of the objects are interesting to improve the quality of the predictive model effectively. In this paper, we propose a new method of active learning of data stream classifier. Its quality has been compared with benchmark solutions based on a large number of test streams, and the results obtained prove the usefulness of the proposed method, especially in the case of a low budget dedicated to the labeling of incoming objects.