Experiments on Ensembles with Missing and Noisy Data

@inproceedings{Melville2004ExperimentsOE,
  title={Experiments on Ensembles with Missing and Noisy Data},
  author={Prem Melville and Nishit Shah and Lilyana Mihalkova and Raymond J. Mooney},
  booktitle={Multiple Classifier Systems},
  year={2004}
}
One of the potential advantages of multiple classifier systems is an increased robustness to noise and other imperfections in data. Previous experiments on classification noise have shown that bagging is fairly robust but that boosting is quite sensitive. Decorate is a recently introduced ensemble method that constructs diverse committees using artificial data. It has been shown to generally outperform both boosting and bagging when training data is limited. This paper compares the sensitivity… CONTINUE READING

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