Title :
Classification of Concept Drift Data Streams
Author :
Padmalatha, E. ; Reddy, C.R.K. ; Rani, B. Padmaja
Abstract :
Concept drift has been a very important concept in the realm of data streams. Streaming data may consist of multiple drifting concepts each having its own underlying data distribution. Concept drift occurs when a set of examples has legitimate class labels at one time and has different legitimate labels at another time. This paper provides a comprehensive overview of existing concept -evolution in concept drifting techniques along different dimensions and it provides lucid vision about the ensemble´s behavior when dealing with concept drifts. Key words:data stream,ensemble, class label,concept drift.
Keywords :
data handling; data mining; pattern classification; statistical distributions; concept drift data stream classification; concept evolution; data distribution; data mining; ensemble behavior; legitimate class labels; probability distribution; Accuracy; Bagging; Classification algorithms; Data mining; Filtering; Real-time systems; Training;
Conference_Titel :
Information Science and Applications (ICISA), 2014 International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4799-4443-9
DOI :
10.1109/ICISA.2014.6847374
