Drift Adaptation via Joint Distribution Alignment
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019, 2019, 00, pp. 498-504
- Issue Date:
- 2019-11-01
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© 2019 IEEE. Machine learning in evolving environment faces challenges due to concept drift. Most concept drift adaptation methods focus on modifying the model. In this paper, a method, Drift Adaptation via Joint Distribution Alignment (DAJDA), is proposed. DAJDA performs a linear transformation to the drift instances instead of modifying model. Instances are transformed into a common feature space, reducing the discrepancy of distributions before and after drift. Experimental studies show that DAJDA has abilities to improve the performance of learning model under concept drift.
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