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dc.contributor.authorSaini, Harsh
dc.contributor.authorLal, Sunil Pranit
dc.contributor.authorNaidu, Vimal Vikash
dc.contributor.authorPickering, Vincel Wince
dc.contributor.authorSingh, Gurmeet
dc.contributor.authorTsunoda, Tatsuhiko
dc.contributor.authorSharma, Alok
dc.date.accessioned2018-07-30T01:30:21Z
dc.date.available2018-07-30T01:30:21Z
dc.date.issued2016
dc.identifier.issn1755-8794
dc.identifier.doi10.1186/s12920-016-0233-2
dc.identifier.urihttp://hdl.handle.net/10072/101149
dc.description.abstractBackground: High dimensional feature space generally degrades classification in several applications. In this paper, we propose a strategy called gene masking, in which non-contributing dimensions are heuristically removed from the data to improve classification accuracy. Methods: Gene masking is implemented via a binary encoded genetic algorithm that can be integrated seamlessly with classifiers during the training phase of classification to perform feature selection. It can also be used to discriminate between features that contribute most to the classification, thereby, allowing researchers to isolate features that may have special significance. Results: This technique was applied on publicly available datasets whereby it substantially reduced the number of features used for classification while maintaining high accuracies. Conclusion: The proposed technique can be extremely useful in feature selection as it heuristically removes non-contributing features to improve the performance of classifiers.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherBioMed Central
dc.relation.ispartofpagefrom261
dc.relation.ispartofpageto269
dc.relation.ispartofissue74
dc.relation.ispartofjournalBMC Medical Genomics
dc.relation.ispartofvolume9
dc.subject.fieldofresearchGenetics not elsewhere classified
dc.subject.fieldofresearchGenetics
dc.subject.fieldofresearchMedical Biochemistry and Metabolomics
dc.subject.fieldofresearchOncology and Carcinogenesis
dc.subject.fieldofresearchcode060499
dc.subject.fieldofresearchcode0604
dc.subject.fieldofresearchcode1101
dc.subject.fieldofresearchcode1112
dc.titleGene masking - A technique to improve accuracy for cancer classification with high dimensionality in microarray data
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Author(s) 2016. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/ zero/1.0/) applies to the data made available in this article, unless otherwise stated.
gro.hasfulltextFull Text
gro.griffith.authorSharma, Alok


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