| Title: |
Greedy Regression Ensemble Selection: Theory and an Application to Water Quality |
| Author(s): |
I. Partalas, G. Tsoumakas, E. Hatzikos, I. Vlahavas.
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| Availability: |
Click here to download the PDF (Acrobat Reader) file.
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| Keywords: |
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| Appeared in: |
Information Sciences, Elsevier, 178(20), pp. 3867-3879, 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. |
| See also : |
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This paper has been cited by the following:
| 1 |
Hernandez-Lobato, D. (2009) Prediction Based on Averages over Automatically Induced Learners: Ensemble Methods and Bayesian Techniques, PhD Thesis, Computer Science Department, Autonomous University of Madrid. |
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Xuan, W., Lv, J., Xie, D. (2010) A hybrid approach of support vector machine with particle swarm optimization for water quality prediction, ICCSE 2010 - 5th International Conference on Computer Science and Education, Final Program and Book of Abstracts, art. no. 5593697, pp. 1158-1163. |
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Iliadis, L.S., Spartalis, S.I., Tachos, S. (2010) Kernel methods and neural networks for water resources management, Environmental Engineering and Management Journal, 9 (2), pp. 181-187. |
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| 9 |
Barros, R.C., Ruiz, D.D., Basgalupp, M.P. (2011) Evolutionary model trees for handling continuous classes in machine learning. Inf. Sci. 181, 5 (March 2011), 954-971. |
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Sun, M., Chen, J., Li, D. (2012) Water temperature prediction in sea cucumber aquaculture ponds by RBF neural network model, Proc. 2012 International Conference on Systems and Informatics, ICSAI 2012, art. no. 6223239, pp. 1154-1159.
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