New ensemble method for classification of data streams

Classification of data streams has become an important area of data mining, as the number of applications facing these challenges increases. In this paper, we propose a new ensemble learning method for data stream classification in presence of concept drift. Our method is capable of detecting changes and adapting to new concepts which appears in the stream.