Machine Learning
Wrappers for feature subset selection
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COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Data mining: practical machine learning tools and techniques with Java implementations
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Machine Learning
Model Combination in the Multiple-Data-Batches Scenario
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ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
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COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
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ACM SIGKDD Explorations Newsletter
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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Wrappers have recently been used to obtain parameter optimizations for learning algorithms. In this paper we investigate the use of a wrapper for estimating the correct number of boosting ensembles in the presence of class noise. Contrary to the naive approach that would be quadratic in the number of boosting iterations, the incremental algorithm described is linear.Additionally, directly using the k-sized ensembles generated during k-fold cross-validation search for prediction usually results in further improvements in classification performance. This improvement can be attributed to the reduction of variance due to averaging k ensembles instead of using only one ensemble. Consequently, cross-validation in the way we use it here, termed wrapping, can be viewed as yet another ensemble learner similar in spirit to bagging but also somewhat related to stacking.