Algorithm to handle Concept Drifting in Data Stream Mining
نویسندگان
چکیده
Data Stream Mining is the evolving field of research. Mining continuous data streams brings unique opportunities but also new challenges. This paper will describe and evaluate the proposed classifier which uses ensemble classifier along with the boosting concept. Adaptive windowing is also used for handling the data stream. Empirical study will show that the proposed classifier takes less memory, less time, gives the good accuracy also handles the concept drift.
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تاریخ انتشار 2013