Ensemble Selection for Water Quality Prediction

I. Partalas, E. Hatzikos, G. Tsoumakas, I. Vlahavas (2007) “Ensemble Selection for Water Quality Prediction”, Proceedings of the 10th International Conference on Engineering Applications of Neural Networks (EANN 2007), pp 428-435, Thessaloniki, Greece, August 29-31, 2007.

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.

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.