Knowledge Maintenance on Data Streams with Concept Drifting

Concept drifting in data streams often occurs unpredictably at any time. Currently many classification mining algorithms deal with this problem by using an incremental learning approach or ensemble classifiers approach. However, both of them can not make a prediction at any time exactly. In this paper, we propose a novel strategy for the maintenance of knowledge. Our approach stores and maintains knowledge in ambiguous decision table with current statistical indicators. With our disambiguation algorithm, a decision tree without any time problem can be synthesized on the fly efficiently. Our experiment results have shown that the accuracy rate of our approach is higher and smoother than other approaches. So, our algorithm is demonstrated to be a real anytime approach.