DocumentCode
2724254
Title
Induction Tree methods to classify M. tuberculosis spoligotypes
Author
Valétudie, Georges
Author_Institution
Univ. Antilles-Guyane, Pointe-a-Pitre Guadeloupe
fYear
2007
fDate
March 1 2007-April 5 2007
Firstpage
101
Lastpage
106
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on
Conference_Location
Honolulu, HI
Print_ISBN
1-4244-0705-2
Type
conf
DOI
10.1109/CIDM.2007.368859
Filename
4221283
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