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dc.contributor.authorKhan, Ahmad Jaffar
dc.contributor.authorRaza, Basit
dc.contributor.authorShahid, Ahmad Raza
dc.contributor.authorKumar, Yogan Jaya
dc.contributor.authorFaheem, Muhammad
dc.contributor.authorAlquhayz, Hani
dc.date.accessioned2022-03-03T11:32:04Z
dc.date.available2022-03-03T11:32:04Z
dc.date.issued2021en_US
dc.identifier.issn1088-467X
dc.identifier.issn1571-4128
dc.identifier.urihttps://doi.org/10.3233/IDA-205331
dc.identifier.urihttps://hdl.handle.net/20.500.12573/1224
dc.description.abstractAlmost all real-world datasets contain missing values. Classification of data with missing values can adversely affect the performance of a classifier if not handled correctly. A common approach used for classification with incomplete data is imputation. Imputation transforms incomplete data with missing values to complete data. Single imputation methods are mostly less accurate than multiple imputation methods which are often computationally much more expensive. This study proposes an imputed feature selected bagging (IFBag) method which uses multiple imputation, feature selection and bagging ensemble learning approach to construct a number of base classifiers to classify new incomplete instances without any need for imputation in testing phase. In bagging ensemble learning approach, data is resampled multiple times with substitution, which can lead to diversity in data thus resulting in more accurate classifiers. The experimental results show the proposed IFBag method is considerably fast and gives 97.26% accuracy for classification with incomplete data as compared to common methods used.en_US
dc.language.isoengen_US
dc.publisherIOS PRESSNIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDSen_US
dc.relation.isversionof10.3233/IDA-205331en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectIncomplete dataen_US
dc.subjectmachine learningen_US
dc.subjectdata classificationen_US
dc.subjectfeature selectionen_US
dc.subjectensemble learningen_US
dc.titleHandling incomplete data classification using imputed feature selected bagging (IFBag) methoden_US
dc.typearticleen_US
dc.contributor.departmentAGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.contributor.institutionauthorKumar, Yogan Jaya
dc.contributor.institutionauthorFaheem, Muhammad
dc.identifier.volumeVolume 25 Issue 4 Page 825-846en_US
dc.relation.journalINTELLIGENT DATA ANALYSISen_US
dc.relation.publicationcategoryMakale - Uluslararası - Editör Denetimli Dergien_US


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