Online Ensemble Learning of Data Streams with Gradually Evolved Classes
Open Access
- 8 February 2016
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Knowledge and Data Engineering
- Vol. 28 (6), 1532-1545
- https://doi.org/10.1109/tkde.2016.2526675
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.Keywords
Funding Information
- 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 Wolfson Research Merit Award
This publication has 41 references indexed in Scilit:
- Recurrent concepts in data streams classificationKnowledge and Information Systems, 2013
- Ambiguous decision trees for mining concept-drifting data streamsPattern Recognition Letters, 2009
- Learn$^{++}$.NC: Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New ClassesIEEE Transactions on Neural Networks, 2008
- Learning from Time-Changing Data with Adaptive WindowingPublished by Society for Industrial & Applied Mathematics (SIAM) ,2007
- Mining data streamsACM SIGMOD Record, 2005
- Online Learning with KernelsIEEE Transactions on Signal Processing, 2004
- Class imbalances versus small disjunctsACM SIGKDD Explorations Newsletter, 2004
- Hybrid decision treeKnowledge-Based Systems, 2002
- Learn++: an incremental learning algorithm for supervised neural networksIEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 2001
- Learning in the presence of concept drift and hidden contextsMachine Learning, 1996