Regularization and Averaging of the Selective Naïve Bayes classifier

  title={Regularization and Averaging of the Selective Naïve Bayes classifier},
  author={Marc Boull{\'e}},
  journal={The 2006 IEEE International Joint Conference on Neural Network Proceedings},
The Nai've Bayes classifier has proved to be very effective on many real data applications. Its performances usually benefit from an accurate estimation of univariate conditional probabilities and from variable selection. However, although variable selection is a desirable feature, it is prone to overfitting. In this paper, we introduce a new regularization technique to select the most probable subset of variables and propose a new model averaging method. The weighting scheme on the models… CONTINUE READING
Highly Cited
This paper has 17 citations. REVIEW CITATIONS


Publications referenced by this paper.
Showing 1-10 of 22 references

Model Selection Workshop and Performance Prediction Challenge

  • I Guyon
  • IEEE World Congress on Computational Intelligence
  • 2006
Highly Influential
3 Excerpts

Design of experiments of the NIPS 2003 variable selection benchmark

  • I Guyon
  • Design of experiments of the NIPS 2003 variable…
  • 2003

Similar Papers

Loading similar papers…