Machine Learning
Ensemble selection from libraries of models
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Getting the Most Out of Ensemble Selection
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Artificial Intelligence Review
Metalearning: Applications to Data Mining
Metalearning: Applications to Data Mining
Bagging ensemble selection for regression
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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Ensemble selection has recently appeared as a popular ensemble learning method, not only because its implementation is fairly straightforward, but also due to its excellent predictive performance on practical problems. The method has been highlighted in winning solutions of many data mining competitions, such as the Netflix competition, the KDD Cup 2009 and 2010, the UCSD FICO contest 2010, and a number of data mining competitions on the Kaggle platform. In this paper we present a novel variant: bagging ensemble selection. Three variations of the proposed algorithm are compared to the original ensemble selection algorithm and other ensemble algorithms. Experiments with ten real world problems from diverse domains demonstrate the benefit of the bagging ensemble selection algorithm.