  The proposed feature selection method builds a histogram of the most stable
features from random subsets of a training set and ranks the features based on
a classifier based cross-validation. This approach reduces the instability of
features obtained by conventional feature selection methods that occur with
variation in training data and selection criteria. Classification results on
four microarray and three image datasets using three major feature selection
criteria and a naive Bayes classifier show considerable improvement over
benchmark results.
