Title :
Using diversity to handle concept drift in on-line learning
Author :
Minku, Fernanda L. ; Yao, Xin
Author_Institution :
Sch. of Comput. Sci., Univ. of Birmingham, Birmingham, UK
Abstract :
A recent study of diversity using online ensembles of learning machines on the presence of concept drift shows that different diversity levels are required before and after a drift. Besides, studies from the dynamic optimisation problems area suggest that, if the best solution for a particular time step is adopted, it may lead to a future scenario in which low accuracy is obtained. Based on that, we propose in this paper a new online ensemble learning approach to handle concept drift, which uses ensembles containing different diversity levels. Even though a high diversity ensemble may have low accuracy while the concept is stable, it may present better accuracy after a drift. The proposed approach successfully chooses the ensemble to be used when a concept drift occurs and shows to obtain better accuracy than a system which adopts the strategy of learning a new classifier from scratch when a drift is detected (strategy adopted by many of the current approaches that explicitly use a drift detection method).
Keywords :
learning (artificial intelligence); optimisation; pattern classification; concept drift handling; diversity level; dynamic optimisation problem; learning machine; online ensemble; pattern classification; Application software; Computer security; Information analysis; Information filtering; Machine learning; Microprocessors; Neural networks; Time factors; Training data; User interfaces;
Conference_Titel :
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-3548-7
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2009.5179008