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dc.contributor.authorGolchin, Maryam
dc.contributor.authorDavarpanah, Seyed Hashem
dc.contributor.authorLiew, Alan Wee-Chung
dc.date.accessioned2017-06-27T02:24:17Z
dc.date.available2017-06-27T02:24:17Z
dc.date.issued2015
dc.identifier.isbn9781467372213
dc.identifier.issn2160-133X
dc.identifier.doi10.1109/ICMLC.2015.7340608
dc.identifier.urihttp://hdl.handle.net/10072/340863
dc.description.abstractClustering is an unsupervised learning technique that groups data into clusters using the entire conditions. However, sometimes, data is similar only under a subset of conditions. Biclustering allows clustering of rows and columns of a dataset simultaneously. It extracts more accurate information from sparse datasets. In recent years, biclustering has found many useful applications in different fields and many biclustering algorithms have been proposed. Using both row and column information of data, biclustering requires the optimization of two conflicting objectives. In this study, a new multi-objective evolutionary biclustering framework using SPEA2 is proposed. A heuristic local search based on the gene and condition deletion and addition is added into SPEA2 and the best bicluster is selected using a new quantitative measure that considers both its coherence and size. The performance of our algorithm is evaluated using simulated and gene expression data and compared with several well-known biclustering methods. The experimental results demonstrate better performance with respect to the size and MSR of detected biclusters and significant enrichment of detected genes.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States of America
dc.relation.ispartofconferencenameInternational Conference on Machine Learning and Cybernetics (ICMLC)
dc.relation.ispartofconferencetitlePROCEEDINGS OF 2015 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOL. 2
dc.relation.ispartofdatefrom2015-07-12
dc.relation.ispartofdateto2015-07-15
dc.relation.ispartoflocationGuangzhou, PEOPLES R CHINA
dc.relation.ispartofpagefrom505
dc.relation.ispartofpagefrom6 pages
dc.relation.ispartofpageto510
dc.relation.ispartofpageto6 pages
dc.relation.ispartofedition1st
dc.relation.ispartofvolume2
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classified
dc.subject.fieldofresearchcode080199
dc.titleBiclustering analysis of gene expression data using multi-objective evolutionary algorithms
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorLiew, Alan Wee-Chung
gro.griffith.authorGolchin, Maryam
gro.griffith.authorDavarpanah, Seyed H.


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    Contains papers delivered by Griffith authors at national and international conferences.

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