Comparing distance measures with visual methods

The selection of the distance measure to separate the objects of the knowledge space is critical in many classification algorithms. In this paper, we analyze the distance measures reported in the literature for the problem of HIV prediction. We propose a new distance for HIV viral sequences, based on the mutations with regard to the HXB2 reference sequence. In a first step, we reduce data dimensionality in order to subsequently analyze the distance measure's performance in terms of its ability to separate classes. © 2008 Springer Berlin Heidelberg.