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Classification Tree Generation Constrained with Variable Weights

  • Pedro Barahona
  • Gemma Bel-Enguix
  • Veronica Dahl
  • M. Dolores Jiménez-López
  • Ludwig Krippahl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6686)

Abstract

Trees are a useful framework for classifying entities whose attributes are, at least partially, related through a common ancestry, such as species of organisms, family members or languages. In some common applications, such as phylogenetic trees based on DNA sequences, relatedness can be inferred from the statistical analysis of unweighted attributes. In this paper we present a Constraint Programming approach that can enforce consistency between bounds on the relative weight of each trait and tree topologies, so that the user can best determine which sets of traits to use and how the entities are likely to be related.

Keywords

Phylogenetic Tree Relative Weight Tree Generation Variable Weight Constraint Program 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Pedro Barahona
    • 1
  • Gemma Bel-Enguix
    • 2
  • Veronica Dahl
    • 2
    • 3
  • M. Dolores Jiménez-López
    • 2
  • Ludwig Krippahl
    • 1
  1. 1.Departamento de InformáticaUniversidade Nova de LisboaPortugal
  2. 2.Research Group on Mathematical LinguisticsUniversitat Rovira i VirgiliPortugal
  3. 3.Department of Computer ScienceSimon Fraser UniversityCanada

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