Popular Ensemble Methods: An Empirical Study

  title={Popular Ensemble Methods: An Empirical Study},
  author={Richard Maclin and David W. Opitz},
  journal={J. Artif. Intell. Res.},
An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Schapire, 1996; Schapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets… CONTINUE READING
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