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Published in:
2011 | OriginalPaper | Chapter
Trading-Off Diversity and Accuracy for Optimal Ensemble Tree Selection in Random Forests
Authors : Haytham Elghazel, Alex Aussem, Florence Perraud
Published in: Ensembles in Machine Learning Applications
Publisher: Springer Berlin Heidelberg
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We discuss an effective method for optimal ensemble tree selection in Random Forests by trading-off diversity and accuracy of the ensemble during the selection process. As the chances of overfitting increase dramatically with the size of the ensemble, we wrap cross-validation around the ensemble selection to maximize the amount of validation data considering, in turn, each fold as a validation fold to select the trees from. The aim is to increase performance by reducing the variance of the tree ensemble selection process. We demonstrate the effectiveness of our approach on several UCI and real-world data sets.