Identification of important features and data mining classification techniques in predicting employee absenteeism at work.
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| Title: | Identification of important features and data mining classification techniques in predicting employee absenteeism at work. |
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| Authors: | Al-Rasheed, Amal1, aaalrasheed@pnu.edu.sa |
| Source: | International Journal of Electrical & Computer Engineering (2088-8708); Oct2021, Vol. 11 Issue 5, p4587-4596, 10p |
| Database: | Applied Science & Technology Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: aci DbLabel: Applied Science & Technology Source An: 150461924 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=150461924 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.11591/ijece.v11i5.pp4587-4596 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 4587 Titles: – TitleFull: Identification of important features and data mining classification techniques in predicting employee absenteeism at work. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Al-Rasheed, Amal IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 20888708 Numbering: – Type: volume Value: 11 – Type: issue Value: 5 Titles: – TitleFull: International Journal of Electrical & Computer Engineering (2088-8708) Type: main |
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