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Combining Bagging and Random Subspaces to Create Better Ensembles

  • Panče Panov
  • Sašo Džeroski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4723)

Abstract

Random forests are one of the best performing methods for constructing ensembles. They derive their strength from two aspects: using random subsamples of the training data (as in bagging) and randomizing the algorithm for learning base-level classifiers (decision trees). The base-level algorithm randomly selects a subset of the features at each step of tree construction and chooses the best among these. We propose to use a combination of concepts used in bagging and random subspaces to achieve a similar effect. The latter randomly select a subset of the features at the start and use a deterministic version of the base-level algorithm (and is thus somewhat similar to the randomized version of the algorithm). The results of our experiments show that the proposed approach has a comparable performance to that of random forests, with the added advantage of being applicable to any base-level algorithm without the need to randomize the latter.

Keywords

Random Forest Bootstrap Sample Ensemble Method Baseline Method Vote Scheme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Panče Panov
    • 1
  • Sašo Džeroski
    • 1
  1. 1.Department of Knowledge Technologies, Jožef Stefan Institute, LjubljanaSlovenia

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