Title :
Induction Tree methods to classify M. tuberculosis spoligotypes
Author :
Valétudie, Georges
Author_Institution :
Univ. Antilles-Guyane, Pointe-a-Pitre Guadeloupe
fDate :
March 1 2007-April 5 2007
Abstract :
In this paper we compared and analyzed four graph induction methods to automatically classify spoligotypes. A spoligotype is a sequence of 43 binary values provided by a DNA analysis technique. This method is known to be useful and efficient to many supervised learning problems. We found it interesting to use these techniques especially for sequential data, in order to create a classifier based on one decision rule per class
Keywords :
biology; diseases; pattern classification; trees (mathematics); DNA analysis; M. tuberculosis spoligotype classification; binary values; decision rule; graph induction methods; induction tree methods; sequential data; Classification tree analysis; Computational intelligence; DNA; Data mining; Decision trees; Genetics; Helium; Sequences; Supervised learning; Tree graphs;
Conference_Titel :
Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0705-2
DOI :
10.1109/CIDM.2007.368859
