TCR-M: A Topic Change Recognition-based Method for Data Stream Learning

Abstract

Data stream learning has received more and more attention in recent years, change tracking and real-time prediction of data streams under uncertainty have been highly focused. With the development of the information age, more and more different types of data streams have been generated, bringing challenges to the research in this field. Among them, text data streams, as one of the categories, also need to be mined and predicted in real-time. This paper addresses this problem by proposing a topic change recognition-based method (TCR-M) for data stream learning, thus helping support text data stream learning. We first propose a topic change recognition process that extracts the topics of the text data stream at each time point, tracks and determines the severity of the topic change, and locates the time points when significant changes occur. Next, an ensemble learning model is constructed and a separate base learner is simultaneously trained to correct the prediction results of the ensemble learning model, which is updated based on the topic change recognition results. To verify the effectiveness of the method, a number of text data streams are collected for evaluation, then outputting the topic change recognition results and prediction results. By comparing it with benchmark methods, the proposed method shows its efficiency. In future research, further improvements are needed for learning and application.