Article ID Journal Published Year Pages File Type
395938 Information Sciences 2008 13 Pages PDF
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.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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