Authors: Snehlata S. Dongre; Latesh G. Malik; Achamma Thomas
Addresses: Department of Computer Science and Engineering, GHRCE Nagpur, Nagpur, MS, 440016, India ' Department of Computer Science and Engineering, Govt. College of Engineering, Nagpur, MS, 441108, India ' Department of Computer Science and Engineering, GHRCE Nagpur, Nagpur, MS, 440016, India
Abstract: In evolving data stream, when its concept undergoes a change it is known as concept drift. Detecting concept drift and handling it is a challenging task in data stream mining. If an algorithm is not adapted to concept drift, then it directly affects its performance. A number of algorithms have been developed to handle concept drift, but they are not suited for both sudden concept drift and gradual concept drift. Thus, there is a demand for an algorithm that can react to both the types of concept drift as well as incur less computational cost. A new approach hybrid early drift detection method (HEDDM) has been proposed for drift detection, which works with an ensemble method to improve the performance.
Keywords: concept drift; data stream; classification; ensemble classifier; concept drift detection; drift detection method; early drift detection method; DDM; EDDM; hybrid early drift detection method; HEDDM; data stream mining; evolving data stream.
International Journal of Intelligent Engineering Informatics, 2019 Vol.7 No.2/3, pp.164 - 179
Received: 13 Jan 2017
Accepted: 17 Sep 2017
Published online: 05 Apr 2019 *