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Algorithms for Feature Selection in Rank-Order Spaces

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dc.contributor.authorSlotta, Douglas J.en
dc.contributor.authorVergara, John Paul C.en
dc.contributor.authorRamakrishnan, Narenen
dc.contributor.authorHeath, Lenwood S.en
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2013-06-19T14:36:04Zen
dc.date.available2013-06-19T14:36:04Zen
dc.date.issued2005en
dc.description.abstractThe problem of feature selection in supervised learning situations is considered, where all features are drawn from a common domain and are best interpreted via ordinal comparisons with other features, rather than as numerical values. In particular, each instance is a member of a space of ranked features. This problem is pertinent in electoral, financial, and bioinformatics contexts, where features denote assessments in terms of counts, ratings, or rankings. Four algorithms for feature selection in such rank-order spaces are presented; two are information-theoretic, and two are order-theoretic. These algorithms are empirically evaluated against both synthetic and real world datasets. The main results of this paper are (i) characterization of relationships and equivalences between different feature selection strategies with respect to the spaces in which they operate, and the distributions they seek to approximate; (ii) identification of computationally simple and efficient strategies that perform surprisingly well; and (iii) a feasibility study of order-theoretic feature selection for large scale datasets.en
dc.format.mimetypeapplication/pdfen
dc.identifierhttp://eprints.cs.vt.edu/archive/00000714/en
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00000714/01/tech_report.pdfen
dc.identifier.trnumberTR-05-08en
dc.identifier.urihttp://hdl.handle.net/10919/20191en
dc.language.isoenen
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectBioinformaticsen
dc.subjectAlgorithmsen
dc.subjectData structuresen
dc.titleAlgorithms for Feature Selection in Rank-Order Spacesen
dc.typeTechnical reporten
dc.type.dcmitypeTexten

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