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Title: Greedy Regression Ensemble Selection: Theory and an Application to Water Quality
Author(s): I. Partalas, G. Tsoumakas, E. Hatzikos, I. Vlahavas.
Availability: Click here to download the PDF (Acrobat Reader) file.
Appeared in: Information Sciences, Elsevier, 178(20), pp. 3867-3879, 2008.
Abstract: This paper studies the greedy ensemble selection family of algorithms for ensembles of regression models. These algorithms search for the globally best subset of regressors by making local greedy decisions for changing the current subset. We abstract the key points of the greedy ensemble selection algorithms and present a general framework, which is applied to an application domain with important social and commercial value: water quality prediction.
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