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Generation of classification trees from variable weighted features

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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. The vast majority of mutations that survive across generations are evolutionarily neutral, which means that most genetic differences between species will have accumulated independently and randomly. In these cases, it is possible to calculate the tree from a precomputed matrix of distances. In other cases, such as with anatomical traits or languages, the assumption of random and independent differences does not hold, making it necessary to consider some traits to be more relevant than others for determining how related two entities are. 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.

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Acknowledgments

This paper has been supported by Project HP2008-0029 and by Portuguese national funds through FCT-Fundação para a Ciência e Tecnologia, under Project PTDC/EIA-CCO/115999/2009.

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Authors and Affiliations

  1. Departamento de Informática, Universidade Nova de Lisboa, Quinta da Torre, 2829-516, Caparica, Portugal

    Pedro Barahona & Ludwig Krippahl

  2. Laboratoire d’Informatique Fondamentale, CNRS-Aix-Marseille University, Avenue de Luminy, 163, 13288, Marseille, France

    Gemma Bel-Enguix

  3. Department of Computer Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada

    Veronica Dahl

  4. Research Group on Mathematical Linguistics, Universitat Rovira i Virgili, Av. Catalunya, 35, 43002, Tarragona, Spain

    M. Dolores Jiménez-López

Authors
  1. Pedro Barahona
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  2. Gemma Bel-Enguix
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  3. Veronica Dahl
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  4. M. Dolores Jiménez-López
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  5. Ludwig Krippahl
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Corresponding author

Correspondence to M. Dolores Jiménez-López.

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Barahona, P., Bel-Enguix, G., Dahl, V. et al. Generation of classification trees from variable weighted features. Nat Comput 13, 169–177 (2014). https://doi.org/10.1007/s11047-013-9368-7

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  • DOI: https://doi.org/10.1007/s11047-013-9368-7

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