Abstract
In a dynamic environment, the data are changed almost instantly. It is difficult and time-consuming to find the correlation between data. At the same time, the concept drift might happen along with data change in the dynamic environment. In order to stimulate the highly correlated data to support better prediction and detected the concept drift, this study proposes a distributed dynamic data driven Application system (DDDAS)-based dynamic weighted majority (DWM) algorithm to solve the issue. The proposed algorithm tries to find the correlations between data by DWM. Moreover, it is capable of detecting concept drift. The simulation result shows the DDDAS-based DWM algorithm has up to 89% accuracy in simulation case, and able to find the concept drift.
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CITATION STYLE
Lin, S. Y., & Lin, C. H. (2015). A dynamic weighted majority algorithm for dynamic data relationships concept drift detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9191, pp. 613–622). Springer Verlag. https://doi.org/10.1007/978-3-319-20895-4_57
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