Abstract

Recent years have witnessed an averaging probability ensemble (AP) classifier which is based on the learnable assumption, although this ensemble classifier is an efficient algorithm for mining concept-drifting data streams, it is still inadequate to represent real-world data streams with noisy data. In this paper, we propose an ensemble classifier framework (WEAP-II) for mining concept-drifting data streams with noise, our theoretically and empirical study shows that WEAP-II is superior to averaging probability ensemble for noisy data streams.

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