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Ensemble based on accuracy and diversity weighting for evolving data streams
Joint Authors
Sun, Yange
Shao, Han
Zhang, Bencai
Source
The International Arab Journal of Information Technology
Issue
Vol. 19, Issue 1 (31 Jan. 2022), pp.90-96, 7 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2022-01-31
Country of Publication
Jordan
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
Ensemble classification is an actively researched paradigm that has received much attention due to increasing real-world applications.
The crucial issue of ensemble learning is to construct a pool of base classifiers with accuracy and diversity.
In this paper, unlike conventional data-streams oriented ensemble methods, we propose a novel Measure via both Accuracy and Diversity (MAD) instead of one of them to supervise ensemble learning.
Based on MAD, a novel online ensemble method called Accuracy and Diversity weighted Ensemble (ADE) effectively handles concept drift in data streams.
ADE mainly uses the following three steps to construct a concept-drift oriented ensemble: for the current data window, 1) a new base classifier is constructed based on the current concept when drift detect, 2) MAD is used to measure the performance of ensemble members, and 3) a newly built classifier replaces the worst base classifier.
If the newly constructed classifier is the worst one, the replacement has not occurred.
Comparing with the state-of-art algorithms, ADE exceeds the current best-related algorithm by 2.38% in average classification accuracy.
Experimental results show that the proposed method can effectively adapt to different types of drifts.
American Psychological Association (APA)
Sun, Yange& Shao, Han& Zhang, Bencai. 2022. Ensemble based on accuracy and diversity weighting for evolving data streams. The International Arab Journal of Information Technology،Vol. 19, no. 1, pp.90-96.
https://search.emarefa.net/detail/BIM-1437420
Modern Language Association (MLA)
Sun, Yange…[et al.]. Ensemble based on accuracy and diversity weighting for evolving data streams. The International Arab Journal of Information Technology Vol. 19, no. 1 (Jan. 2022), pp.90-96.
https://search.emarefa.net/detail/BIM-1437420
American Medical Association (AMA)
Sun, Yange& Shao, Han& Zhang, Bencai. Ensemble based on accuracy and diversity weighting for evolving data streams. The International Arab Journal of Information Technology. 2022. Vol. 19, no. 1, pp.90-96.
https://search.emarefa.net/detail/BIM-1437420
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 95-96
Record ID
BIM-1437420