New ensemble method for classification of data streams
Parinaz Sobhani, Hamid Beigy · 2011
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.