Variable selection using random forests
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| Title: | Variable selection using random forests |
|---|---|
| Authors: | Genuer, Robin1 Robin.Genuer@math.u-psud.fr, Poggi, Jean-Michel1,2 Jean-Michel.Poggi@math.u-psud.fr, Tuleau-Malot, Christine3 malot@unice.fr |
| Source: | Pattern Recognition Letters. Oct2010, Vol. 31 Issue 14, p2225-2236. 12p. |
| Subjects: | Statistics, Regression analysis, Prediction theory, Tree graphs, Mathematical models, Mathematical variables |
| Abstract: | Abstract: This paper proposes, focusing on random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001, to investigate two classical issues of variable selection. The first one is to find important variables for interpretation and the second one is more restrictive and try to design a good parsimonious prediction model. The main contribution is twofold: to provide some experimental insights about the behavior of the variable importance index based on random forests and to propose a strategy involving a ranking of explanatory variables using the random forests score of importance and a stepwise ascending variable introduction strategy. [Copyright &y& Elsevier] |
| Copyright of Pattern Recognition Letters is the property of Elsevier B.V. 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: 53405232 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Variable selection using random forests – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Genuer%2C+Robin%22">Genuer, Robin</searchLink><relatesTo>1</relatesTo><i> Robin.Genuer@math.u-psud.fr</i><br /><searchLink fieldCode="AR" term="%22Poggi%2C+Jean-Michel%22">Poggi, Jean-Michel</searchLink><relatesTo>1,2</relatesTo><i> Jean-Michel.Poggi@math.u-psud.fr</i><br /><searchLink fieldCode="AR" term="%22Tuleau-Malot%2C+Christine%22">Tuleau-Malot, Christine</searchLink><relatesTo>3</relatesTo><i> malot@unice.fr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Pattern+Recognition+Letters%22">Pattern Recognition Letters</searchLink>. Oct2010, Vol. 31 Issue 14, p2225-2236. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+theory%22">Prediction theory</searchLink><br /><searchLink fieldCode="DE" term="%22Tree+graphs%22">Tree graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+variables%22">Mathematical variables</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: This paper proposes, focusing on random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001, to investigate two classical issues of variable selection. The first one is to find important variables for interpretation and the second one is more restrictive and try to design a good parsimonious prediction model. The main contribution is twofold: to provide some experimental insights about the behavior of the variable importance index based on random forests and to propose a strategy involving a ranking of explanatory variables using the random forests score of importance and a stepwise ascending variable introduction strategy. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Pattern Recognition Letters is the property of Elsevier B.V. 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.patrec.2010.03.014 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 2225 Subjects: – SubjectFull: Statistics Type: general – SubjectFull: Regression analysis Type: general – SubjectFull: Prediction theory Type: general – SubjectFull: Tree graphs Type: general – SubjectFull: Mathematical models Type: general – SubjectFull: Mathematical variables Type: general Titles: – TitleFull: Variable selection using random forests Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Genuer, Robin – PersonEntity: Name: NameFull: Poggi, Jean-Michel – PersonEntity: Name: NameFull: Tuleau-Malot, Christine IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 10 Text: Oct2010 Type: published Y: 2010 Identifiers: – Type: issn-print Value: 01678655 Numbering: – Type: volume Value: 31 – Type: issue Value: 14 Titles: – TitleFull: Pattern Recognition Letters Type: main |
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