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.
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Bonet, I., Rodríguez, A., Grau, R., García, M.M., Saez, Y., Nowé, A. (2008). Comparing Distance Measures with Visual Methods. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_8
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DOI: https://doi.org/10.1007/978-3-540-88636-5_8
Publisher Name: Springer, Berlin, Heidelberg
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