Learning Ensembles from Bites: A Scalable and Accurate Approach

@article{Chawla2004LearningEF,
  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},
  year={2004},
  volume={5},
  pages={421-451}
}
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
Highly Cited
This paper has 163 citations. REVIEW CITATIONS

From This Paper

Topics from this paper.

Citations

Publications citing this paper.
Showing 1-10 of 75 extracted citations

163 Citations

01020'06'09'12'15'18
Citations per Year
Semantic Scholar estimates that this publication has 163 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 36 references

Pasting bites together for prediction in large data sets

  • L. Breiman
  • Machine Learning,
  • 1999
Highly Influential
12 Excerpts

Similar Papers

Loading similar papers…