Online Ensemble Learning of Data Streams with Gradually Evolved Classes

• Published in: IEEE transactions on knowledge and data engineering Vol. 28; no. 6; pp. 1532 - 1545
• Main Authors: Yu Sun, Ke Tang, Minku, Leandro L., Shuo Wang, Xin Yao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation; Adaptation models; Adjustment; Biological evolution; class evolution; Computer science; Data mining; Data models; data stream mining; Data transmission; Distance learning; Dynamics; ensemble model; Evolution; Handles; imbalanced classification; on-line learning; Probability distribution; Sun; Transient analysis; imbalanced classification; ensemble model; class evolution; on-line learning; Data stream mining
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2016.2526675

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.