Abstract
Traditional data manipulation models such as Bagging and Boosting select training cases from throughout the problem space to generate diversity and improve performance. A new data manipulation model is proposed that dynamically assigns specialists to train on difficult clusters of training data. The model allows the expertise of specialists to overlap for difficult regions of the problem. It has been coupled with a dynamic combination model to exploit the diversity of specialist members. The model has been applied to an environmental problem and has demonstrated that dynamic modelling can enhance both the diversity of members and the accuracy of the ensemble.
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© 2006 Springer-Verlag Berlin Heidelberg
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Spencer, M., McCullagh, J., Whitfort, T. (2006). Clustering Data Manipulation Method for Ensembles. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_134
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DOI: https://doi.org/10.1007/11941439_134
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
Online ISBN: 978-3-540-49788-2
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