Causal inference and Bayesian network structure learning from nominal data.
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| Title: | Causal inference and Bayesian network structure learning from nominal data. |
|---|---|
| Authors: | Luo, Guiming1, Zhao, Boxu1 boxu.zhao@foxmail.com, Du, Shiyuan1 |
| Source: | Applied Intelligence. Jan2019, Vol. 49 Issue 1, p253-264. 12p. |
| Subjects: | Bayesian analysis, Structural learning theory, Discrete systems, Integrated circuit interconnections, Mobile communication systems |
| Abstract: | This study investigates a discrete causal method for nominal data (DCMND) which is one of the important issues of causal inference. It is utilized to learn the causal Bayesian network to reflect the interconnections between variables in our paper. This article also proposes a Bayesian network construction algorithm based on discrete causal inference (BDCI) and an extended BDCI Bayesian network construction algorithm based on DCMND. Furthermore, the paper studies the alarm data of mobile communication system in practice. The results suggest that decision criterion based our method is effective in causal inference and the Bayesian network constructed by our method has better classification accuracy compared to other methods. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Intelligence is the property of Springer Nature 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 134239528 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Causal inference and Bayesian network structure learning from nominal data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Luo%2C+Guiming%22">Luo, Guiming</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Boxu%22">Zhao, Boxu</searchLink><relatesTo>1</relatesTo><i> boxu.zhao@foxmail.com</i><br /><searchLink fieldCode="AR" term="%22Du%2C+Shiyuan%22">Du, Shiyuan</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Intelligence%22">Applied Intelligence</searchLink>. Jan2019, Vol. 49 Issue 1, p253-264. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+learning+theory%22">Structural learning theory</searchLink><br /><searchLink fieldCode="DE" term="%22Discrete+systems%22">Discrete systems</searchLink><br /><searchLink fieldCode="DE" term="%22Integrated+circuit+interconnections%22">Integrated circuit interconnections</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+communication+systems%22">Mobile communication systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study investigates a discrete causal method for nominal data (DCMND) which is one of the important issues of causal inference. It is utilized to learn the causal Bayesian network to reflect the interconnections between variables in our paper. This article also proposes a Bayesian network construction algorithm based on discrete causal inference (BDCI) and an extended BDCI Bayesian network construction algorithm based on DCMND. Furthermore, the paper studies the alarm data of mobile communication system in practice. The results suggest that decision criterion based our method is effective in causal inference and the Bayesian network constructed by our method has better classification accuracy compared to other methods. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Intelligence is the property of Springer Nature 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=134239528 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10489-018-1274-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 253 Subjects: – SubjectFull: Bayesian analysis Type: general – SubjectFull: Structural learning theory Type: general – SubjectFull: Discrete systems Type: general – SubjectFull: Integrated circuit interconnections Type: general – SubjectFull: Mobile communication systems Type: general Titles: – TitleFull: Causal inference and Bayesian network structure learning from nominal data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Luo, Guiming – PersonEntity: Name: NameFull: Zhao, Boxu – PersonEntity: Name: NameFull: Du, Shiyuan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 0924669X Numbering: – Type: volume Value: 49 – Type: issue Value: 1 Titles: – TitleFull: Applied Intelligence Type: main |
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