Abstract
Concept drift is the recent trend of online data. The distribution underlying the data is changing with time .There are many algorithms developed in the literature to handle such drifting data concepts. In our paper we are outlining the framework of our new approach to handle drifts which will be based on the concept of diversity. Diversity is the measure of variation in the predictive accuracy of ensemble members. Our approach would implement diversity concept first time on the online approach that does not explicitly use a mechanism to handle drifts. This type of online approach would give better accuracy at a slight increase in the running time and memory. In our paper we would also outline the main objectives behind our research and the state of the art in data stream mining.
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Sidhu, P., Bhatia, M.P.S. (2014). Extended Dynamic Weighted Majority Using Diversity to Handle Drifts. In: Catania, B., et al. New Trends in Databases and Information Systems. Advances in Intelligent Systems and Computing, vol 241. Springer, Cham. https://doi.org/10.1007/978-3-319-01863-8_41
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DOI: https://doi.org/10.1007/978-3-319-01863-8_41
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01862-1
Online ISBN: 978-3-319-01863-8
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