Dynamic weighted majority for incremental learning of imbalanced data streams with concept drift?

Status	已發表Published
	Dynamic weighted majority for incremental learning of imbalanced data streams with concept drift?
	Lu Y.2; Cheung Y.-M.2; Tang Y.Y.3
	2017
Source Publication	IJCAI International Joint Conference on Artificial Intelligence
Pages	2393-2399
Abstract	Concept drifts occurring in data streams will jeopardize the accuracy and stability of the online learning process. If the data stream is imbalanced, it will be even more challenging to detect and cure the concept drift. In the literature, these two problems have been intensively addressed separately, but have yet to be well studied when they occur together. In this paper, we propose a chunk-based incremental learning method called Dynamic Weighted Majority for Imbalance Learning (DWMIL) to deal with the data streams with concept drift and class imbalance problem. DWMIL utilizes an ensemble framework by dynamically weighting the base classifiers according to their performance on the current data chunk. Compared with the existing methods, its merits are four-fold: (1) it can keep stable for non-drifted streams and quickly adapt to the new concept; (2) it is totally incremental, i.e. no previous data needs to be stored; (3) it keeps a limited number of classifiers to ensure high efficiency; and (4) it is simple and needs only one thresholding parameter. Experiments on both synthetic and real data sets with concept drift show that DWMIL performs better than the state-of-the-art competitors, with less computational cost.
URL	View the original
Language	英語English
Fulltext Access	View Full-Text via Scopus
Document Type	Conference paper
Collection	University of Macau
Affiliation	1.HKBU Institute of Research and Continuing Education 2.Hong Kong Baptist University 3.Universidade de Macau
Recommended Citation GB/T 7714	Lu Y.,Cheung Y.-M.,Tang Y.Y.. Dynamic weighted majority for incremental learning of imbalanced data streams with concept drift?[C], 2017, 2393-2399.
APA	Lu Y.., Cheung Y.-M.., & Tang Y.Y. (2017). Dynamic weighted majority for incremental learning of imbalanced data streams with concept drift?. IJCAI International Joint Conference on Artificial Intelligence, 2393-2399.

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