Learning Ensembles from Bites: A Scalable and Accurate Approach

  • Authors:
  • Nitesh V. Chawla;Lawrence O. Hall;Kevin W. Bowyer;W. Philip Kegelmeyer

  • Affiliations:
  • -;-;-;-

  • Venue:
  • The Journal of Machine Learning Research
  • Year:
  • 2004

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Abstract

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 classifiers on small subsets of data in a distributed environment. Experiments show this approach is fast, accurate, and scalable.