Exploiting Unlabeled Data for Improving Accuracy of Predictive Data Mining

@inproceedings{Peng2003ExploitingUD,
  title={Exploiting Unlabeled Data for Improving Accuracy of Predictive Data Mining},
  author={Kang Peng and Slobodan Vucetic and Bo Han and Hongbo Xie and Zoran Obradovic},
  booktitle={ICDM},
  year={2003}
}
Predictive data mining typically relies on labeled data without exploiting a much larger amount of availabl e unlabeled data. The goal of this paper is to show t hat using unlabeled data can be beneficial in a range o f important prediction problems and therefore should be an integral part of the learning process. Given an unl abeled dataset representative of the underlying distributi on and a K-class labeled sample that might be biased, our approach is to learn K contrast classifiers each tr… CONTINUE READING

Citations

Publications citing this paper.
Showing 1-10 of 12 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 29 references

H

  • S. Vucetic, D. Pokrajac
  • Xie and Z. Obradov ic, “Detection of…
  • 2003
Highly Influential
4 Excerpts

Probabilistic outputs for suppor t vector machines and comparison to regularized likelihood methods

  • J. C. Platt
  • Advances in Large Margin Classifiers , A. J…
  • 1999
Highly Influential
4 Excerpts

M

  • B. Boeckmann, A. Bairoch, R. Apweiler
  • C. Bl atter, A. Estreicher, E. Gasteiger, M. J…
  • 2003
1 Excerpt

Similar Papers

Loading similar papers…