Abstract: Mining time-changing data streams is of great interest. The fundamental problems are how to effectively identify the significant changes and organize new training data to adjust the outdated model. In this paper, we propose an active learning system to address these issues. Without need knowing any true labels of the new data, we devise an active approach to detecting the possible changes. Whenever the suspected changes are indicated, it exploits a light-weight uncertainty sampling algorithm to choose the most informative instances to label. With these labeled instances, it further tests the truth of the suspected changes. If the changes indeed cause significant performance deterioration of the current model, it evolves the old model. Thus, our method is sensitive to significant changes and robust to noisy changes, and can quickly adapt to concept-drift. Experimental results from both synthetic and real-world data confirm the advantages of our system.
Keywords: Ming time-changing data streams, significant changes, active learning, uncertainty sampling, increasing iterative-step size, concept-drift
