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Title: Online Ensemble Learning of Data Streams with Gradually Evolved Classes
Authors: Sun, Y.
Tang, K.
Minku, Leandro Lei
Wang, S.
Yao, X.
First Published: 8-Feb-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE), United States
Citation: IEEE Transactions on Knowledge and Data Engineering, 2016, 28 (6), pp. 1532-1545
Abstract: Class evolution, the phenomenon of class emergence and disappearance, is an important research topic for data stream mining. All previous studies implicitly regard class evolution as a transient change, which is not true for many real-world problems. This paper concerns the scenario where classes emerge or disappear gradually. A class-based ensemble approach, namely Class-Based ensemble for Class Evolution (CBCE), is proposed. By maintaining a base learner for each class and dynamically updating the base learners with new data, CBCE can rapidly adjust to class evolution. A novel under-sampling method for the base learners is also proposed to handle the dynamic class-imbalance problem caused by the gradual evolution of classes. Empirical studies demonstrate the effectiveness of CBCE in various class evolution scenarios in comparison to existing class evolution adaptation methods.
DOI Link: 10.1109/TKDE.2016.2526675
ISSN: 1041-4347
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Deposited with reference to the publisher’s archiving policy available on the SHERPA/RoMEO website.
Appears in Collections:Published Articles, Dept. of Computer Science

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