data distribution changes with time. As a result, the concept drifts occur very frequently in the
data stream. Concept drifts in data stream increase the challenges in learning as well, it also
significantly decreases the accuracy of the classifier. However, recently many algorithms
have been proposed that exclusively designed for data stream mining while considering
drifting concept in the data stream. This paper presents an empirical evaluation of these …