Handling Concept Drift in Data Stream Classification.

Abstract

Data Streams are having huge volume and it can-not be stored permanently in the memory for processing. In this paper we would be mainly focusing on issues in data stream, the major factors which are affecting the accuracy of classifier like imbalance class and Concept Drift. The drift in Data Stream mining refers to the change in data. Such as Class imbalance problem notifies that the samples are in the classes are not equal. In our research work we are trying to identify the change (Drift) in data, we are trying to detect Imbalance class and noise from changed data. And According to the type of drift we are applying the algorithms and trying to make the stream more balance and noise free to improve classifier’s accuracy.