Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/3106
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGheyas, Iffat Aen_UK
dc.contributor.authorSmith, Leslieen_UK
dc.date.accessioned2016-12-09T02:56:19Z-
dc.date.available2016-12-09T02:56:19Z-
dc.date.issued2010-10en_UK
dc.identifier.urihttp://hdl.handle.net/1893/3106-
dc.description.abstractThe treatment of incomplete data is an important step in the pre-processing of data. We propose a novel nonparametric algorithm Generalized regression neural network Ensemble for Multiple Imputation (GEMI). We also developed a single imputation (SI) version of this approach-GESI. We compare our algorithms with 25 popular missing data imputation algorithms on 98 real-world and synthetic datasets for various percentage of missing values. The effectiveness of the algorithms is evaluated in terms of (i) the accuracy of output classification: three classifiers (a generalized regression neural network, a multilayer perceptron and a logistic regression technique) are separately trained and tested on the dataset imputed with each imputation algorithm, (ii) interval analysis with missing observations and (iii) point estimation accuracy of the missing value imputation. GEMI outperformed GESI and all the conventional imputation algorithms in terms of all three criteria considered.en_UK
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.relationGheyas IA & Smith L (2010) A neural network-based framework for the reconstruction of incomplete data sets. 10th Brazilian Symposium on Neural Networks (SBRN2008), Salvador, Brazil, 26.10.2008-30.10.2008. Neurocomputing, 73 (16-18), pp. 3039-3065. https://doi.org/10.1016/j.neucom.2010.06.021en_UK
dc.relation.urihttp://dblp2.uni-trier.de/db/conf/sbrn/sbrn2008.htmlen_UK
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author; you can only request a copy if you wish to use this work for your own research or private study.en_UK
dc.subjectMissing valuesen_UK
dc.subjectImputationen_UK
dc.subjectSingle imputationen_UK
dc.subjectMultiple imputationen_UK
dc.subjectGeneralized regression neural networksen_UK
dc.subjectNeural networks (Computer science)en_UK
dc.subjectData miningen_UK
dc.titleA neural network-based framework for the reconstruction of incomplete data setsen_UK
dc.typeConference Paperen_UK
dc.rights.embargodate2997-12-31en_UK
dc.rights.embargoreason[Gheyas_Smith_Neurocomputing_19102010.pdf] The publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.en_UK
dc.identifier.doi10.1016/j.neucom.2010.06.021en_UK
dc.citation.jtitleNeurocomputingen_UK
dc.citation.issn0925-2312en_UK
dc.citation.volume73en_UK
dc.citation.issue16-18en_UK
dc.citation.spage3039en_UK
dc.citation.epage3065en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.author.emaillss@cs.stir.ac.uken_UK
dc.citation.conferencedates2008-10-26 - 2008-10-30en_UK
dc.citation.conferencelocationSalvador, Brazilen_UK
dc.citation.conferencename10th Brazilian Symposium on Neural Networks (SBRN2008)en_UK
dc.citation.date31/10/2008en_UK
dc.contributor.affiliationUniversity of Stirlingen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.identifier.isiWOS:000294092200027en_UK
dc.identifier.scopusid2-s2.0-78650175506en_UK
dc.identifier.wtid829856en_UK
dc.contributor.orcid0000-0002-3716-8013en_UK
dc.date.accepted2009-05-20en_UK
dc.date.filedepositdate2011-06-23en_UK
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

Files in This Item:
File Description SizeFormat 
Gheyas_Smith_Neurocomputing_19102010.pdfFulltext - Published Version1.33 MBAdobe PDFUnder Embargo until 2997-12-31    Request a copy


This item is protected by original copyright



Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.