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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.
Keywords
distance measure, HIV, similarity, MDS, kNN

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Chicago
Bonet, Isis, Abdel Rodríguez, Ricardo Grau, Maria M García, Yvan Saeys, and Ann Nowé. 2008. “Comparing Distance Measures with Visual Methods.” In Lecture Notes in Artificial Intelligence, ed. Alexander Gelbukh and Eduardo F Morales, 5317:90–99. Berlin, Germany: Springer.
APA
Bonet, Isis, Rodríguez, A., Grau, R., García, M. M., Saeys, Y., & Nowé, A. (2008). Comparing distance measures with visual methods. In Alexander Gelbukh & E. F. Morales (Eds.), Lecture Notes in Artificial Intelligence (Vol. 5317, pp. 90–99). Presented at the 7th Mexican international conference on Artificial Intelligence (MICAI 2008), Berlin, Germany: Springer.
Vancouver
1.
Bonet I, Rodríguez A, Grau R, García MM, Saeys Y, Nowé A. Comparing distance measures with visual methods. In: Gelbukh A, Morales EF, editors. Lecture Notes in Artificial Intelligence. Berlin, Germany: Springer; 2008. p. 90–9.
MLA
Bonet, Isis, Abdel Rodríguez, Ricardo Grau, et al. “Comparing Distance Measures with Visual Methods.” Lecture Notes in Artificial Intelligence. Ed. Alexander Gelbukh & Eduardo F Morales. Vol. 5317. Berlin, Germany: Springer, 2008. 90–99. Print.
@inproceedings{2104475,
  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.},
  author       = {Bonet, Isis and Rodr{\'i}guez, Abdel and Grau, Ricardo and Garc{\'i}a, Maria M and Saeys, Yvan and Now{\'e}, Ann},
  booktitle    = {Lecture Notes in Artificial Intelligence},
  editor       = {Gelbukh, Alexander and Morales, Eduardo F},
  isbn         = {9783540886358},
  issn         = {0302-9743},
  keyword      = {distance measure,HIV,similarity,MDS,kNN},
  language     = {eng},
  location     = {Atizap{\'a}n de Zaragoza, Mexico},
  pages        = {90--99},
  publisher    = {Springer},
  title        = {Comparing distance measures with visual methods},
  url          = {http://dx.doi.org/10.1007/978-3-540-88636-5\_8},
  volume       = {5317},
  year         = {2008},
}

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