Greedy regression ensemble selection: Theory and an application to water quality prediction

Authors:
Ioannis Partalas;Grigorios Tsoumakas;Evaggelos V. Hatzikos;Ioannis Vlahavas
Affiliations:
Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;Department of Automation, Technical Education, Institute of Thessaloniki, Greece;Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Venue:
Information Sciences: an International Journal
Year:
2008

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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.