Feature selection is done using cross validation on the training documents.
we partition the training documents into 9 folds, train the classifier whose features are to be selected on 8 folds, apply the classifier to the remaining fold, and repeat this process 9 times in order to select the feature groups that have the best average performance over the 9 folds when used in combination with the baseline features.