Structural Learning of Bayesian Networks Via Constrained Hill Climbing Algorithms: Adjusting Trade-off between Efficiency and Accuracy.
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| Title: | Structural Learning of Bayesian Networks Via Constrained Hill Climbing Algorithms: Adjusting Trade-off between Efficiency and Accuracy. |
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| Authors: | Arias, Jacinto1, Gámez, José A.1 jose.gamez@uclm.es, Puerta, José M.1 jose.puerta@uclm.es |
| Source: | International Journal of Intelligent Systems. Mar2015, Vol. 30 Issue 3, p292-325. 34p. |
| Subjects: | Structural learning theory, Bayesian analysis, Hill climbing algorithms, Machine learning, Search algorithms, Data analysis |
| Abstract: | Learning Bayesian networks is known to be an NP-hard problem, and this, combined with the growing interest in learning models from high-dimensional domains, leads to the necessity of finding more efficient learning algorithms. Recent papers have proposed constrained approaches of successfully and widely used local search algorithms, such as Hill Climbing. One of these algorithms families, called constrained Hill Climbing (CHC), greatly improves upon the efficiency of the original approach, obtaining models with slightly lower quality but maintaining their theoretical properties. In this paper, we propose three different modifications to the most scalable version of these algorithms, fast constrained Hill Climbing, to improve the quality of its output by relaxing the constraints imposed to include some diversification in the search process. The aim of these new approaches is to adjust the trade-off between efficiency and accuracy of the algorithm, as they do not modify its complexity and only imply a few more search iterations. We perform an intensive experimental evaluation of the modifications proposed with an extensive comparison between the original algorithms and the new modifications covering several scenarios with quite large data sets. Available code and data for further use of the algorithms presented in this paper and experiment replication can be available at . [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 100488463 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Structural Learning of Bayesian Networks Via Constrained Hill Climbing Algorithms: Adjusting Trade-off between Efficiency and Accuracy. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Arias%2C+Jacinto%22">Arias, Jacinto</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Gámez%2C+José+A%2E%22">Gámez, José A.</searchLink><relatesTo>1</relatesTo><i> jose.gamez@uclm.es</i><br /><searchLink fieldCode="AR" term="%22Puerta%2C+José+M%2E%22">Puerta, José M.</searchLink><relatesTo>1</relatesTo><i> jose.puerta@uclm.es</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Intelligent+Systems%22">International Journal of Intelligent Systems</searchLink>. Mar2015, Vol. 30 Issue 3, p292-325. 34p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Structural+learning+theory%22">Structural learning theory</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Hill+climbing+algorithms%22">Hill climbing algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Search+algorithms%22">Search algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Learning Bayesian networks is known to be an NP-hard problem, and this, combined with the growing interest in learning models from high-dimensional domains, leads to the necessity of finding more efficient learning algorithms. Recent papers have proposed constrained approaches of successfully and widely used local search algorithms, such as Hill Climbing. One of these algorithms families, called constrained Hill Climbing (CHC), greatly improves upon the efficiency of the original approach, obtaining models with slightly lower quality but maintaining their theoretical properties. In this paper, we propose three different modifications to the most scalable version of these algorithms, fast constrained Hill Climbing, to improve the quality of its output by relaxing the constraints imposed to include some diversification in the search process. The aim of these new approaches is to adjust the trade-off between efficiency and accuracy of the algorithm, as they do not modify its complexity and only imply a few more search iterations. We perform an intensive experimental evaluation of the modifications proposed with an extensive comparison between the original algorithms and the new modifications covering several scenarios with quite large data sets. Available code and data for further use of the algorithms presented in this paper and experiment replication can be available at . [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=100488463 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/int.21701 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 34 StartPage: 292 Subjects: – SubjectFull: Structural learning theory Type: general – SubjectFull: Bayesian analysis Type: general – SubjectFull: Hill climbing algorithms Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Search algorithms Type: general – SubjectFull: Data analysis Type: general Titles: – TitleFull: Structural Learning of Bayesian Networks Via Constrained Hill Climbing Algorithms: Adjusting Trade-off between Efficiency and Accuracy. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Arias, Jacinto – PersonEntity: Name: NameFull: Gámez, José A. – PersonEntity: Name: NameFull: Puerta, José M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2015 Type: published Y: 2015 Identifiers: – Type: issn-print Value: 08848173 Numbering: – Type: volume Value: 30 – Type: issue Value: 3 Titles: – TitleFull: International Journal of Intelligent Systems Type: main |
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