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Publication Details

  Author(s): I. Partalas, E. Hatzikos, G. Tsoumakas, I. Vlahavas.

Title: “Ensemble Selection for Water Quality Prediction”.

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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