Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5934
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dc.contributor.authorGao, S.-
dc.contributor.authorAddam, O.-
dc.contributor.authorQabaja A.-
dc.contributor.authorElsheikh A.-
dc.contributor.authorZarour O.-
dc.contributor.authorNagi M.-
dc.contributor.authorAlhajja R.-
dc.date.accessioned2021-09-11T15:20:52Z-
dc.date.available2021-09-11T15:20:52Z-
dc.date.issued2012en_US
dc.identifier.citation2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012, 9 May 2012 through 12 May 2012, San Diego, CA, 91207en_US
dc.identifier.isbn9781467311892-
dc.identifier.urihttps://doi.org/10.1109/CIBCB.2012.6217219-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/5934-
dc.description.abstractGenes are encoding regions that form essential building block within the cell and lead to proteins which are achieving various functions. However, some genes may be mutated due to internal or external factors and this is a main cause for various diseases. The latter case could be discovered by closely examining samples taken from patients to identify faulty genes. In other words, it is important to identify mutated genes as disease biomarkers. Then consider certain normal and infected samples to build a classifier model capable of successfully classifying new samples as infected or normal. The work described in this paper addresses this problem by introducing a comprehensive framework that incorporates the two stages of the process, namely feature selection and sample classification. In fact, high dimensionality in terms of the number of genes and small number of samples distinguishes gene expression data as an ideal application for the proposed framework. Reducing the dimensionality is essential to efficiently analysis the samples for effective knowledge discovery. Actually, there is a tradeoff between feature selection and maintaining acceptable accuracy. The target is to find the reduction level or compact set of features which once used for knowledge discovery will lead to improved performance and acceptable accuracy. For the first stage, we concentrate on four feature selection techniques, namely chi-square from statistics, frequent pattern mining and clustering from data mining, and community detection from network analysis. The effectiveness of the feature reduction techniques is demonstrated in the second stage by coupling them with classification techniques, namely associative classification, support vector machine and naive Bayesian classifier. Majority voting is applied for both stages. The results reported for four cancer datasets demonstrate the applicability and effectiveness of the proposed framework. © 2012 IEEE.en_US
dc.description.sponsorshipIEEE Computational Intelligence Societyen_US
dc.language.isoenen_US
dc.relation.ispartof2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectassociative classifieren_US
dc.subjectchisquareen_US
dc.subjectclassificationen_US
dc.subjectclusteringen_US
dc.subjectFeature selectionen_US
dc.subjectfrequent pattern miningen_US
dc.subjectgene expression dataen_US
dc.subjectnaive Bayesian classifieren_US
dc.subjectnetwork analysisen_US
dc.subjectSVMen_US
dc.titleRobust integrated framework for effective feature selection and sample classification and its application to gene expression data analysisen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.startpage112en_US
dc.identifier.endpage119en_US
dc.identifier.scopus2-s2.0-84864033066en_US
dc.institutionauthorÖzyer, Tansel-
dc.identifier.doi10.1109/CIBCB.2012.6217219-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.relation.conference2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012en_US
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
item.cerifentitytypePublications-
item.languageiso639-1en-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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