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Title: Ensemble Selection for Water Quality Prediction
Author(s): I. Partalas, E. Hatzikos, G. Tsoumakas, I. Vlahavas.
Availability: Click here to download the PDF (Acrobat Reader) file (8 pages).
Appeared in: Proceedings of the 10th International Conference on Engineering Applications of Neural Networks, Thessaloniki, 2007.
Abstract: This paper studies the greedy ensemble selection algorithm for ensembles of regression models. We explore two interesting parameters of this algorithm: a) the direction of search (forward, backward), and b) the performance evaluation dataset (training set, validation set) on a large ensemble (200 models) of neural networks and support vector machines. Experimental comparison of the different parameters are performed on an application domain with important social and commercial value: water quality monitoring. In specific we experiment on real data collected from an underwater sensor system.
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1 Bin Zeng, Zhaohui Luo, Jun Wei, Sea Water Pollution Assessment Based On Ensemble of Classifiers, Fourth International Conference on Natural Computation, 2008.
2 KJ Kim, SB Cho, Ensemble Approaches in Evolutionary Game Strategies: A Case Study in Othello, IEEE Symposium on Computational Intelligence and Games, 2008.
3 Wu, J. (2012) Prediction of rainfall time series using modular RBF neural network model coupled with SSA and PLS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7197 LNAI (PART 2), pp. 509-518.