Classification tree generation constrained with variable weights

Ludwig Krippahl; Pedro Manuel Corrêa Calvente de Barahona

doi:10.1007/978-3-642-21344-1_29

Classification tree generation constrained with variable weights

Ludwig Krippahl, Pedro Manuel Corrêa Calvente de Barahona

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 ...

Original language	Unknown
Title of host publication	Lecture notes in computer science
Pages	274-283
ISBN (Electronic)	978-3-642-21344-1
DOIs	https://doi.org/10.1007/978-3-642-21344-1_29
Publication status	Published - 1 Jan 2011
Event	4th international conference on Interplay between natural and artificial computation - Duration: 1 Jan 2011 → …

Conference

Conference	4th international conference on Interplay between natural and artificial computation
Period	1/01/11 → …

Access to Document

10.1007/978-3-642-21344-1_29

Cite this

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title = "Classification tree generation constrained with variable weights",

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 ...",

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AB - 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 ...

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