An Empirical Comparison of Three Boosting Algorithms on Real Data Sets with Artificial Class Noise

@inproceedings{McDonald2003AnEC,
  title={An Empirical Comparison of Three Boosting Algorithms on Real Data Sets with Artificial Class Noise},
  author={Ross A. McDonald and David J. Hand and Idris A. Eckley},
  booktitle={Multiple Classifier Systems},
  year={2003}
}
Boosting algorithms are a means of building a strong ensemble classiier by aggregating a sequence of weak hypotheses. In this paper we consider three of the best-known boosting algorithms: Adaboost 8], Logitboost 10] and Brownboost 7]. These algorithms are adaptive, and work by maintaining a set of example and class weights which focus the attention of a base learner on the examples that are hardest to classify. We conduct an empirical study to compare the performance of these algorithms… CONTINUE READING

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  • At each stage we record the nal training and test error rates, and report the average errors within a 95% conndence interval.

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