The IBMAP approach for Markov network structure learning

Autores
Schluter, Federico Enrique Adolfo; Bromberg, Facundo; Edera, Alejandro
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work we consider the problem of learning the structure of Markov networks from data. We present an approach for tackling this problem called IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC algorithm, designed for avoiding important limitations of existing independence-based algorithms. These algorithms proceed by performing statistical independence tests on data, trusting completely the outcome of each test. In practice tests may be incorrect, resulting in potential cascading errors and the consequent reduction in the quality of the structures learned. IBMAP contemplates this uncertainty in the outcome of the tests through a probabilistic maximum-a-posteriori approach. The approach is instantiated in the IBMAP-HC algorithm, a structure selection strategy that performs a polynomial heuristic local search in the space of possible structures. We present an extensive empirical evaluation on synthetic and real data, showing that our algorithm outperforms significantly the current independence-based algorithms, in terms of data efficiency and quality of learned structures, with equivalent computational complexities. We also show the performance of IBMAP-HC in a real-world application of knowledge discovery: EDAs, which are evolutionary algorithms that use structure learning on each generation for modeling the distribution of populations. The experiments show that when IBMAP-HC is used to learn the structure, EDAs improve the convergence to the optimum.
Fil: Schluter, Federico Enrique Adolfo. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de información. Laboratorio DHARMa; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Bromberg, Facundo. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de información. Laboratorio DHARMa; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Edera, Alejandro. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de información. Laboratorio DHARMa; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Materia
Markov Network
Structure Learning
Independence Tests
Knowledge Discovery
Edas
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/32280
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/32280
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling The IBMAP approach for Markov network structure learningSchluter, Federico Enrique AdolfoBromberg, FacundoEdera, AlejandroMarkov NetworkStructure LearningIndependence TestsKnowledge DiscoveryEdashttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In this work we consider the problem of learning the structure of Markov networks from data. We present an approach for tackling this problem called IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC algorithm, designed for avoiding important limitations of existing independence-based algorithms. These algorithms proceed by performing statistical independence tests on data, trusting completely the outcome of each test. In practice tests may be incorrect, resulting in potential cascading errors and the consequent reduction in the quality of the structures learned. IBMAP contemplates this uncertainty in the outcome of the tests through a probabilistic maximum-a-posteriori approach. The approach is instantiated in the IBMAP-HC algorithm, a structure selection strategy that performs a polynomial heuristic local search in the space of possible structures. We present an extensive empirical evaluation on synthetic and real data, showing that our algorithm outperforms significantly the current independence-based algorithms, in terms of data efficiency and quality of learned structures, with equivalent computational complexities. We also show the performance of IBMAP-HC in a real-world application of knowledge discovery: EDAs, which are evolutionary algorithms that use structure learning on each generation for modeling the distribution of populations. The experiments show that when IBMAP-HC is used to learn the structure, EDAs improve the convergence to the optimum.Fil: Schluter, Federico Enrique Adolfo. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de información. Laboratorio DHARMa; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Bromberg, Facundo. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de información. Laboratorio DHARMa; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Edera, Alejandro. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de información. Laboratorio DHARMa; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaSpringer2014-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/32280Edera, Alejandro; Bromberg, Facundo; Schluter, Federico Enrique Adolfo; The IBMAP approach for Markov network structure learning; Springer; Annals of Mathematics and Artificial Intelligence; 72; 3-4; 4-2014; 197-2231012-24431573-7470CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007/s10472-014-9419-5info:eu-repo/semantics/altIdentifier/doi/10.1007/s10472-014-9419-5info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-29T11:20:50Zoai:ri.conicet.gov.ar:11336/32280instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-29 11:20:50.632CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv The IBMAP approach for Markov network structure learning
title The IBMAP approach for Markov network structure learning
spellingShingle The IBMAP approach for Markov network structure learning
Schluter, Federico Enrique Adolfo
Markov Network
Structure Learning
Independence Tests
Knowledge Discovery
Edas
title_short The IBMAP approach for Markov network structure learning
title_full The IBMAP approach for Markov network structure learning
title_fullStr The IBMAP approach for Markov network structure learning
title_full_unstemmed The IBMAP approach for Markov network structure learning
title_sort The IBMAP approach for Markov network structure learning
dc.creator.none.fl_str_mv Schluter, Federico Enrique Adolfo
Bromberg, Facundo
Edera, Alejandro
author Schluter, Federico Enrique Adolfo
author_facet Schluter, Federico Enrique Adolfo
Bromberg, Facundo
Edera, Alejandro
author_role author
author2 Bromberg, Facundo
Edera, Alejandro
author2_role author
author
dc.subject.none.fl_str_mv Markov Network
Structure Learning
Independence Tests
Knowledge Discovery
Edas
topic Markov Network
Structure Learning
Independence Tests
Knowledge Discovery
Edas
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this work we consider the problem of learning the structure of Markov networks from data. We present an approach for tackling this problem called IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC algorithm, designed for avoiding important limitations of existing independence-based algorithms. These algorithms proceed by performing statistical independence tests on data, trusting completely the outcome of each test. In practice tests may be incorrect, resulting in potential cascading errors and the consequent reduction in the quality of the structures learned. IBMAP contemplates this uncertainty in the outcome of the tests through a probabilistic maximum-a-posteriori approach. The approach is instantiated in the IBMAP-HC algorithm, a structure selection strategy that performs a polynomial heuristic local search in the space of possible structures. We present an extensive empirical evaluation on synthetic and real data, showing that our algorithm outperforms significantly the current independence-based algorithms, in terms of data efficiency and quality of learned structures, with equivalent computational complexities. We also show the performance of IBMAP-HC in a real-world application of knowledge discovery: EDAs, which are evolutionary algorithms that use structure learning on each generation for modeling the distribution of populations. The experiments show that when IBMAP-HC is used to learn the structure, EDAs improve the convergence to the optimum.
Fil: Schluter, Federico Enrique Adolfo. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de información. Laboratorio DHARMa; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Bromberg, Facundo. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de información. Laboratorio DHARMa; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Edera, Alejandro. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de información. Laboratorio DHARMa; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
description In this work we consider the problem of learning the structure of Markov networks from data. We present an approach for tackling this problem called IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC algorithm, designed for avoiding important limitations of existing independence-based algorithms. These algorithms proceed by performing statistical independence tests on data, trusting completely the outcome of each test. In practice tests may be incorrect, resulting in potential cascading errors and the consequent reduction in the quality of the structures learned. IBMAP contemplates this uncertainty in the outcome of the tests through a probabilistic maximum-a-posteriori approach. The approach is instantiated in the IBMAP-HC algorithm, a structure selection strategy that performs a polynomial heuristic local search in the space of possible structures. We present an extensive empirical evaluation on synthetic and real data, showing that our algorithm outperforms significantly the current independence-based algorithms, in terms of data efficiency and quality of learned structures, with equivalent computational complexities. We also show the performance of IBMAP-HC in a real-world application of knowledge discovery: EDAs, which are evolutionary algorithms that use structure learning on each generation for modeling the distribution of populations. The experiments show that when IBMAP-HC is used to learn the structure, EDAs improve the convergence to the optimum.
publishDate 2014
dc.date.none.fl_str_mv 2014-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/32280
Edera, Alejandro; Bromberg, Facundo; Schluter, Federico Enrique Adolfo; The IBMAP approach for Markov network structure learning; Springer; Annals of Mathematics and Artificial Intelligence; 72; 3-4; 4-2014; 197-223
1012-2443
1573-7470
CONICET Digital
CONICET
url http://hdl.handle.net/11336/32280
identifier_str_mv Edera, Alejandro; Bromberg, Facundo; Schluter, Federico Enrique Adolfo; The IBMAP approach for Markov network structure learning; Springer; Annals of Mathematics and Artificial Intelligence; 72; 3-4; 4-2014; 197-223
1012-2443
1573-7470
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007/s10472-014-9419-5
info:eu-repo/semantics/altIdentifier/doi/10.1007/s10472-014-9419-5
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.10058

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