期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 28, 期 6, 页码 1532-1545出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2016.2526675
关键词
Data stream mining; class evolution; ensemble model; on-line learning; imbalanced classification
类别
资金
- National Natural Science Foundation of China [61329302, 61175065]
- Program for New Century Excellent Talents in University [NCET-12-0512]
- EPSRC [EP/J017515/1]
- Royal Society
- EPSRC [EP/J017515/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/J017515/1] Funding Source: researchfish
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.
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