An Empirical Evaluation of Bagging and Boosting

  title={An Empirical Evaluation of Bagging and Boosting},
  author={Richard Maclin and David W. Opitz},
An ensemble consists of a set of independently trained classi ers such as neural networks or decision trees whose predictions are combined when classifying novel instances Previous re search has shown that an ensemble as a whole is often more accurate than any of the single classi ers in the ensemble Bagging Breiman a and Boosting Freund Schapire are two relatively new but popular methods for produc ing ensembles In this paper we evaluate these methods using both neural networks and decision… CONTINUE READING
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