Abstract
A precondition of existing ensemble-based distributed data mining techniques is the assumption that contributing data are identically and independently distributed. However, this assumption is not valid in many virtual organization contexts because contextual heterogeneity exists. Focusing on regression tasks, this paper proposes a context-based meta-learning technique for horizontally partitioned data with contextual heterogeneity. The predictive performance of our new approach and the state of the art techniques are evaluated and compared on both simulated and real-world data sets.
The support of the Informatics Research Initiative of Enterprise Ireland is gratefully acknowledged.
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Xing, Y., Madden, M.G., Duggan, J., Lyons, G.J. (2005). Context-Sensitive Regression Analysis for Distributed Data. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_35
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DOI: https://doi.org/10.1007/11527503_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27894-8
Online ISBN: 978-3-540-31877-4
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