Feature selection in possibilistic modeling.
Saved in:
| Title: | Feature selection in possibilistic modeling. |
|---|---|
| Authors: | Ammar Bouhamed, S.1 sonda.ammar@gmail.com, Khanfir Kallel, I.1 imen.khanfir.kallel@gmail.com, Sellami Masmoudi, D.1 dorra.masmoudi@enis.rnu.tn, Solaiman, B.2 basel.solaiman@telecom-bretagne.eu |
| Source: | Pattern Recognition. Nov2015, Vol. 48 Issue 11, p3627-3640. 14p. |
| Subjects: | Feature selection, Mathematical models, Computational complexity, Set theory, Distribution (Probability theory) |
| Abstract: | Feature selection is becoming increasingly important for the reduction of computing complexity. In this context, conventional approaches have random performances, because They can succeed for some contexts and fail for others. Possibilistic modeling is a powerful paradigm being able to handle data imperfection or redundancy and is not affected by data variability. Therefore, in this paper, we propose a new feature selection strategy for possibilitic modeling. The proposed approach is based on two issues in order to extract relevant features: the measure of feature importance as well as the possibility distribution uncertainty degree. The importance of one feature can be considered under two aspects: The first one is related to the scattering within one class and the second one reflects the feature power for class discrimination. Therefore, we apply, here, Shapley index paradigm which selects features who minimize the intra-class distance and who maximize the inter-class distance. The previous process is refined using possibility distribution uncertainty degree in order to resolve some conflict problems between feature׳s importance values. [ABSTRACT FROM AUTHOR] |
| Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science 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 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 108433442 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Feature selection in possibilistic modeling. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ammar+Bouhamed%2C+S%2E%22">Ammar Bouhamed, S.</searchLink><relatesTo>1</relatesTo><i> sonda.ammar@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Khanfir+Kallel%2C+I%2E%22">Khanfir Kallel, I.</searchLink><relatesTo>1</relatesTo><i> imen.khanfir.kallel@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Sellami+Masmoudi%2C+D%2E%22">Sellami Masmoudi, D.</searchLink><relatesTo>1</relatesTo><i> dorra.masmoudi@enis.rnu.tn</i><br /><searchLink fieldCode="AR" term="%22Solaiman%2C+B%2E%22">Solaiman, B.</searchLink><relatesTo>2</relatesTo><i> basel.solaiman@telecom-bretagne.eu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Pattern+Recognition%22">Pattern Recognition</searchLink>. Nov2015, Vol. 48 Issue 11, p3627-3640. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+complexity%22">Computational complexity</searchLink><br /><searchLink fieldCode="DE" term="%22Set+theory%22">Set theory</searchLink><br /><searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Feature selection is becoming increasingly important for the reduction of computing complexity. In this context, conventional approaches have random performances, because They can succeed for some contexts and fail for others. Possibilistic modeling is a powerful paradigm being able to handle data imperfection or redundancy and is not affected by data variability. Therefore, in this paper, we propose a new feature selection strategy for possibilitic modeling. The proposed approach is based on two issues in order to extract relevant features: the measure of feature importance as well as the possibility distribution uncertainty degree. The importance of one feature can be considered under two aspects: The first one is related to the scattering within one class and the second one reflects the feature power for class discrimination. Therefore, we apply, here, Shapley index paradigm which selects features who minimize the intra-class distance and who maximize the inter-class distance. The previous process is refined using possibility distribution uncertainty degree in order to resolve some conflict problems between feature׳s importance values. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science 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=108433442 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.patcog.2015.03.015 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 3627 Subjects: – SubjectFull: Feature selection Type: general – SubjectFull: Mathematical models Type: general – SubjectFull: Computational complexity Type: general – SubjectFull: Set theory Type: general – SubjectFull: Distribution (Probability theory) Type: general Titles: – TitleFull: Feature selection in possibilistic modeling. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ammar Bouhamed, S. – PersonEntity: Name: NameFull: Khanfir Kallel, I. – PersonEntity: Name: NameFull: Sellami Masmoudi, D. – PersonEntity: Name: NameFull: Solaiman, B. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2015 Type: published Y: 2015 Identifiers: – Type: issn-print Value: 00313203 Numbering: – Type: volume Value: 48 – Type: issue Value: 11 Titles: – TitleFull: Pattern Recognition Type: main |
| ResultId | 1 |
