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dc.contributor.author
Schluter, Federico Enrique Adolfo
dc.contributor.author
Bromberg, Facundo
dc.contributor.author
Edera, Alejandro
dc.date.available
2018-01-04T13:57:28Z
dc.date.issued
2014-04
dc.identifier.citation
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
dc.identifier.issn
1012-2443
dc.identifier.uri
http://hdl.handle.net/11336/32280
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Springer
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Markov Network
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Structure Learning
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Independence Tests
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Knowledge Discovery
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Edas
dc.subject.classification
Ciencias de la Computación
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Ciencias de la Computación e Información
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CIENCIAS NATURALES Y EXACTAS
dc.title
The IBMAP approach for Markov network structure learning
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2017-11-09T13:35:06Z
dc.identifier.eissn
1573-7470
dc.journal.volume
72
dc.journal.number
3-4
dc.journal.pagination
197-223
dc.journal.pais
Suiza
dc.description.fil
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
dc.description.fil
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
dc.description.fil
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
dc.journal.title
Annals of Mathematics and Artificial Intelligence
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007/s10472-014-9419-5
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/s10472-014-9419-5
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