Title: A data driven trimming procedure for robust classification,
Abstract: Classification rules can be severely affected by the presence of disturbing
observations in the training sample. Looking for an optimal classifier with
such data may lead to unnecessarily complex rules. So, simpler effective
classification rules could be achieved if we relax the goal of fitting a good
rule for the whole training sample but only consider a fraction of the data. In
this paper we introduce a new method based on trimming to produce
classification rules with guaranteed performance on a significant fraction of
the data. In particular, we provide an automatic way of determining the right
trimming proportion and obtain in this setting oracle bounds for the
classification error on the new data set.