Combining One-Class Classifiers to Classify Missing Data

  title={Combining One-Class Classifiers to Classify Missing Data},
  author={Piotr Juszczak and Robert P. W. Duin},
  booktitle={Multiple Classifier Systems},
In the paper a new method for handling with missing features values in classification is presented. The presented idea is to form an ensemble of one-class classifiers trained on each feature, preselected group of features or to compute from features a dissimilarity representation. Thus when any feature values are missing for a data point to be labeled, the ensemble can still make a reasonable decision based on the remaining classifiers. With the comparison to standard algorithms that handle… CONTINUE READING
26 Citations
17 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 26 extracted citations


Publications referenced by this paper.
Showing 1-10 of 17 references

One-class classification

  • D.M.J. Tax
  • PhD thesis, Delft University of Technology
  • 2001
Highly Influential
4 Excerpts

Consistent regression methods for discriminant analysis with incomplete data

  • R.J.A. Little
  • J. Amer. Statist. Assoc. 73
  • 1978
Highly Influential
4 Excerpts

Similar Papers

Loading similar papers…