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dc.creatorLan, L
dc.creatorVucetic, S
dc.date.accessioned2021-01-31T18:12:39Z
dc.date.available2021-01-31T18:12:39Z
dc.date.issued2013-12-20
dc.identifier.issn1753-6561
dc.identifier.issn1753-6561
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/5332
dc.identifier.otherPMC4043987 (pmc)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/5350
dc.description.abstract© 2013 Lan and Vucetic; licensee BioMed Central Ltd. A major challenge in microarray classification is that the number of features is typically orders of magnitude larger than the number of examples. In this paper, we propose a novel feature filter algorithm to select the feature subset with maximal discriminative power and minimal redundancy by solving a quadratic objective function with binary integer constraints. To improve the computational efficiency, the binary integer constraints are relaxed and a low-rank approximation to the quadratic term is applied. The proposed feature selection algorithm was extended to solve multi-task microarray classification problems. We compared the single-task version of the proposed feature selection algorithm with 9 existing feature selection methods on 4 benchmark microarray data sets. The empirical results show that the proposed method achieved the most accurate predictions overall. We also evaluated the multi-task version of the proposed algorithm on 8 multi-task microarray datasets. The multi-task feature selection algorithm resulted in significantly higher accuracy than when using the single-task feature selection methods.
dc.format.extentS5-
dc.language.isoen
dc.relation.haspartBMC Proceedings
dc.relation.isreferencedbySpringer Science and Business Media LLC
dc.rightsCC BY
dc.rights.urihttp://creativecommons.org/licenses/by/2.0
dc.subject0801 Artificial Intelligence and Image Processing
dc.titleMulti-task feature selection in microarray data by binary integer programming
dc.typeArticle
dc.type.genreConference Proceeding
dc.relation.doi10.1186/1753-6561-7-S7-S5
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.date.updated2021-01-31T18:12:36Z
refterms.dateFOA2021-01-31T18:12:40Z


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