Combining One-Class Classifiers to Classify Missing Data

@inproceedings{Juszczak2004CombiningOC,
  title={Combining One-Class Classifiers to Classify Missing Data},
  author={Piotr Juszczak and Robert P. W. Duin},
  booktitle={Multiple Classifier Systems},
  year={2004}
}
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
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