Knowledge-maximized ensemble algorithm for different types of concept drift

Knowledge extraction from data streams has attracted attention in recent years due to its wide range of applications, including sensor networks, web clickstreams, and user interest analysis. Concept drift is one of the most important research topics in data stream mining. Many algorithms that can adapt to concept drift have been proposed. However, most of them specialize in only one type of concept drift and can rarely be used in the environments with a large number of unavailable sample labels. In this study, we propose a new data stream classifier called knowledge-maximized ensemble (KME). First, supervised and unsupervised knowledge are leveraged to detect concept drift, recognize recurrent concepts, and evaluate the weights of ensemble members. Second, the preserved labelled instances in past blocks can be reused to enhance the recognition ability of the candidate member. The final decision for an incoming observation is derived from all the prediction results of the component classifiers. Accordingly, the maximum utilization of the relevant information in a data stream can be achieved, which is critical to models with limited training data. Third, KME can react to multiple types of concept drift by combining the mechanisms of online and chunk-based ensembles. Finally, we compare KME with eight state-of-the-art classifiers on several synthetic and real-world datasets. The comparison demonstrates the effectiveness of KME in various types of concept drift scenarios.