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Online Ensemble Learning of Data Streams with Gradually Evolved Classes.
- Source :
- IEEE Transactions on Knowledge & Data Engineering; Jun2016, Vol. 28 Issue 6, p1532-1545, 14p
- Publication Year :
- 2016
-
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. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 10414347
- Volume :
- 28
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Knowledge & Data Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 115088941
- Full Text :
- https://doi.org/10.1109/TKDE.2016.2526675