Algorithm to handle Concept Drifting in Data Stream Mining

Data Stream Mining is the evolving field of research. Miningcontinuous data streams brings unique opportunities but also newchallenges. This paper will describe and evaluate the proposedclassifier which uses ensemble classifier along with the boostingconcept. Adaptive windowing is also used for handling the datastream. Empirical study will show that the proposed classifiertakes less memory, less time,