Learning Ensembles from Bites: A Scalable and Accurate Approach

  title={Learning Ensembles from Bites: A Scalable and Accurate Approach},
  author={Nitesh V. Chawla and Lawrence O. Hall and Kevin W. Bowyer and W. Philip Kegelmeyer},
  journal={Journal of Machine Learning Research},
Bagging and boosting are two popular ensemble methods that typically achieve better accuracy than a single classifier. These techniques have limitations on massive data sets, because the size of the data set can be a bottleneck. Voting many classifiers built on small subsets of data (“pasting small votes”) is a promising approach for learning from massive data sets, one that can utilize the power of boosting and bagging. We propose a framework for building hundreds or thousands of such… CONTINUE READING
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Pasting bites together for prediction in large data sets

  • L. Breiman
  • Machine Learning,
  • 1999
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