Comparing Distance Measures with Visual Methods

Authors:
Isis Bonet;Abdel Rodríguez;Ricardo Grau;Maria M. García;Yvan Saez;Ann Nowé
Affiliations:
Center of Studies on Informatics, Central University of Las Villas, Santa Clara, Cuba;Center of Studies on Informatics, Central University of Las Villas, Santa Clara, Cuba;Center of Studies on Informatics, Central University of Las Villas, Santa Clara, Cuba;Center of Studies on Informatics, Central University of Las Villas, Santa Clara, Cuba;Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Ghent University, Belgium;Computational Modeling Lab, Brussels University, Belgium
Venue:
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2008

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Abstract

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.