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- W3174463126 abstract "During the past years solar radiation prediction has become increasingly relevant among the scientific community and Machine Learning techniques have proven to be a useful tool to automatically learn an accurate prediction model. In this paper, we move one step further and try to gain interpretability during the learning process by introducing a novel feature selection approach. Our method trains a set of bootstrapped SVR classifiers to detect those features that are informative for the prediction task. This way we obtain a more robust set of selected features compared to other selection methods. This allows us to detect in a multivariate fashion not only the features needed to solve the prediction task, but also those that are informative for the problem at hand. The application of this algorithm to a Weather Research and Forecasting model, and its comparison to some state of the art tools, shows the advantages of the proposed method both in terms of resistance to overfitting, selection consistency and interpretability, while at the same time improving performance in terms of prediction accuracy." @default.
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- W3174463126 date "2016-01-01" @default.
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- W3174463126 title "Feature selection in solar radiation prediction using bootstrapped SVRs" @default.
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