DocumentCode
590953
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
fYear
2011
fDate
13-14 Oct. 2011
Firstpage
264
Lastpage
269
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Knowledge Engineering (ICCKE), 2011 1st International eConference on
Conference_Location
Mashhad
Print_ISBN
978-1-4673-5712-8
Type
conf
DOI
10.1109/ICCKE.2011.6413362
Filename
6413362
Link To Document