Detecting concept drift using HEDDM in data stream. (5th April 2019)

• Record Type: Journal Article
• Authors: Dongre, Snehlata S.; Malik, Latesh G.; Thomas, Achamma
• Is Part Of: International journal of intelligent engineering informatics. Volume 7:Number 2/3(2019)
• Page Start: 164
• Page End: 179
• Publication Date: 2019-04-05
• Subjects: 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; Artificial intelligence -- Engineering applications -- Periodicals; Engineering -- Computer programs -- Periodicals; Knowledge management -- Periodicals; 620.0028563
• Languages: English
• ISSNs: 1758-8715

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.