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Generation of classification trees from variable weighted features
March 2013
BBDJK13
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 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.
Journal
Pedro Barahona, Gemma Bel-Enguix, Verónica Dahl, Maria Dolores Jimenez-Lopez, Ludwig Krippahl
Natural Computing
Springer Science+Business Media
Dordrecht 2013
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1567-7818 (on-line)
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http://link.springer.com/article/10.1007%2Fs11047-013-9368-7
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Pedro Barahona and Gemma Bel-Enguix and Verónica Dahl and Maria Dolores Jimenez-Lopez and Ludwig Krippahl, Generation of classification trees from variable weighted features, Natural Computing, Springer Science+Business Media, Dordrecht 2013, ISSN 1567-7818 (on-line), (http://link.springer.com/article/10.1007%2Fs11047-013-9368-7), March 2013.
<b><a href="/people/members/view.php?code=7e27bc13fad97e99cd21ea6914d55659" class="author">Pedro Barahona</a>, Gemma Bel-Enguix, Verónica Dahl, Maria Dolores Jimenez-Lopez and <a href="/people/members/view.php?code=195d68ea5904b58472fd8c8aedcae233" class="author">Ludwig Krippahl</a></b>, <u>Generation of classification trees from variable weighted features</u>, Natural Computing, Springer Science+Business Media, Dordrecht 2013, ISSN 1567-7818 (on-line), (<a href="http://link.springer.com/article/10.1007%2Fs11047-013-9368-7" target="_blank">url</a>), March 2013.
@article {BBDJK13, author = {Pedro Barahona and Gemma Bel-Enguix and Ver{\'o}nica Dahl and Maria Dolores Jimenez-Lopez and Ludwig Krippahl}, title = {Generation of classification trees from variable weighted features}, journal = {Natural Computing}, publisher = {Springer Science+Business Media}, address = {Dordrecht 2013}, issn = {1567-7818 (on-line)}, url = {http://link.springer.com/article/10.1007%2Fs11047-013-9368-7}, 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 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.}, keywords = {Classification Tees; Constraint Programming; Bioinformatics; Linguistics}, month = {March}, year = {2013}, }
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