A major difficulty in bioinformatics is due to the size of the datasets, which contain frequently
large numbers of variables. In this study, we present a two-step procedure for feature
selection. In a first “filtering” stage, a relatively small subset of features is identified on the
basis of several criteria. In the second stage, the importance of the selected variables is
evaluated based on the frequency of their participation in relevant patterns and low impact
variables are eliminated. This step is applied iteratively, until arriving to a Pareto-optimal …