  When applied to high-dimensional datasets, feature selection algorithms might
still leave dozens of irrelevant variables in the dataset. Therefore, even
after feature selection has been applied, classifiers must be prepared to the
presence of irrelevant variables. This paper investigates a new training method
called Contingency Training which increases the accuracy as well as the
robustness against irrelevant attributes. Contingency training is classifier
independent. By subsampling and removing information from each sample, it
creates a set of constraints. These constraints aid the method to automatically
find proper importance weights of the dataset's features. Experiments are
conducted with the contingency training applied to neural networks over
traditional datasets as well as datasets with additional irrelevant variables.
For all of the tests, contingency training surpassed the unmodified training on
datasets with irrelevant variables and even outperformed slightly when only a
few or no irrelevant variables were present.
