Complexity-driven Evolution of Decision Graphs for Classification of Medical Data.
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| Title: | Complexity-driven Evolution of Decision Graphs for Classification of Medical Data. |
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| Authors: | Podgorelec, Vili1 vili.podgorelec@uni-mb.si |
| Source: | Informatica (03505596). May2005, Vol. 29 Issue 1, p41-51. 11p. 5 Diagrams, 2 Charts, 1 Graph. |
| Subjects: | Data mining, Automatic programming (Computer science), Graph theory, Medical informatics, Algorithms, Decision making |
| Abstract: | In the paper we study the possibility of constructing decision graphs with the help of several meta agents. Decision graphs are an extension of the well known decision trees and introduce the possibility of program nodes and cycles in a classification model. A two-leveled evolutionary algorithm for the induction of decision graphs is presented and the principle of classification based on the decision graphs is described. Several agents are used to construct the decision graphs; they are constructed and evolved with the help of automatic programming and evaluated with a universal complexity measure. The developed model is applied to a medical dataset for the classification of patients with mitral valve prolapse syndrome. [ABSTRACT FROM AUTHOR] |
| Copyright of Informatica (03505596) is the property of Slovene Society Informatika 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 | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 17869346 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Complexity-driven Evolution of Decision Graphs for Classification of Medical Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Podgorelec%2C+Vili%22">Podgorelec, Vili</searchLink><relatesTo>1</relatesTo><i> vili.podgorelec@uni-mb.si</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Informatica+%2803505596%29%22">Informatica (03505596)</searchLink>. May2005, Vol. 29 Issue 1, p41-51. 11p. 5 Diagrams, 2 Charts, 1 Graph. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+programming+%28Computer+science%29%22">Automatic programming (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+theory%22">Graph theory</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+informatics%22">Medical informatics</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In the paper we study the possibility of constructing decision graphs with the help of several meta agents. Decision graphs are an extension of the well known decision trees and introduce the possibility of program nodes and cycles in a classification model. A two-leveled evolutionary algorithm for the induction of decision graphs is presented and the principle of classification based on the decision graphs is described. Several agents are used to construct the decision graphs; they are constructed and evolved with the help of automatic programming and evaluated with a universal complexity measure. The developed model is applied to a medical dataset for the classification of patients with mitral valve prolapse syndrome. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Informatica (03505596) is the property of Slovene Society Informatika 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=17869346 |
| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 41 Subjects: – SubjectFull: Data mining Type: general – SubjectFull: Automatic programming (Computer science) Type: general – SubjectFull: Graph theory Type: general – SubjectFull: Medical informatics Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Decision making Type: general Titles: – TitleFull: Complexity-driven Evolution of Decision Graphs for Classification of Medical Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Podgorelec, Vili IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2005 Type: published Y: 2005 Identifiers: – Type: issn-print Value: 03505596 Numbering: – Type: volume Value: 29 – Type: issue Value: 1 Titles: – TitleFull: Informatica (03505596) Type: main |
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