Title :
New ensemble method for classification of data streams
Author :
Sobhani, P. ; Beigy, Hamid
Author_Institution :
Dept. of Comput. Eng., Sharif Univ. of Technol., Tehran, Iran
Abstract :
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.
Keywords :
data mining; learning (artificial intelligence); pattern classification; change detection; concept drift; data mining; data stream classification; ensemble learning method; ensemble method; Accuracy; Boosting; Classification algorithms; Data mining; Educational institutions; Training data; Data stream classification; boosting; concept drift; ensemble learning;
Conference_Titel :
Computer and Knowledge Engineering (ICCKE), 2011 1st International eConference on
Conference_Location :
Mashhad
Print_ISBN :
978-1-4673-5712-8
DOI :
10.1109/ICCKE.2011.6413362
