A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classification

Here, we present a new feature-selection algorithm based on mixed integer programming methods [2] able to extract multiple and adjacent solutions for supervised learning problems applied to biological data. We focus on those problems where the relative position of a feature (i.e., nucleotide locus) is relevant. In particular, we aim to find sets of distinctive features, which are as close as possible to each other and which appear with the same required characteristics. Our algorithm adopts a fast and effective method to evaluate the quality of the extracted sets of features and it has been successfully integrated in a rule-based classification framework [3].