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      Wrapping boosters against noise

      Pfahringer, Bernhard; Holmes, Geoffrey; Schmidberger, Gabi
      DOI
       10.1007/3-540-45656-2_35
      Link
       www.springerlink.com
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      Citation
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      Pfahringer, B., Holmes, G. & Schmidberger, G.(2001). Wrapping boosters against noise. In Proceedings of 14th Australian Joint Conference on Artificial Intelligence Adelaide, Australia, December 10–14, 2001(pp.593-605). Berlin: Springer.
      Permanent Research Commons link: https://hdl.handle.net/10289/1495
      Abstract
      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.
      Date
      2001
      Type
      Conference Contribution
      Publisher
      Springer, Berlin
      Collections
      • Computing and Mathematical Sciences Papers [1454]
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