Deleting or Keeping Outliers for Classifier Training?

This paper introduces two statistical outlier detection approaches by classes. Experiments on binary and multi-class classification problems reveal that the partial removal of outliers improves significantly one or two performance measures for C4.S and I-nearest neighbour classifiers. Also, a taxonomy of problems according to the amount of outliers is proposed.