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Issue No. 06 - June (2016 vol. 28)
ISSN: 1041-4347
pp: 1532-1545
Yu Sun , USTC-Birmingham Joint Research Institute in Intelligent Computation and its Applications, School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
Ke Tang , USTC-Birmingham Joint Research Institute in Intelligent Computation and its Applications, School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
Leandro L. Minku , Department of Computer Science, University of Leicester, University Road, Leicester, United Kingdom
Shuo Wang , Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science, The University of Birmingham, Edgbaston, Birmingham, United Kingdom
Xin Yao , Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science, The University of Birmingham, Edgbaston, Birmingham, United Kingdom
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.
INDEX TERMS
Data mining, Adaptation models, Data models, Transient analysis, Sun, Probability distribution, Computer science
CITATION

Y. Sun, K. Tang, L. L. Minku, S. Wang and X. Yao, "Online Ensemble Learning of Data Streams with Gradually Evolved Classes," in IEEE Transactions on Knowledge & Data Engineering, vol. 28, no. 6, pp. 1532-1545, 2016.
doi:10.1109/TKDE.2016.2526675
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