Scalable Optimization-Based Feature Selection Using Random Sampling

dc.contributor.author Yang, Jaekyung
dc.contributor.author Olafsson, Sigurdur
dc.contributor.author Olafsson, Sigurdur
dc.contributor.department Industrial and Manufacturing Systems Engineering
dc.date 2018-10-12T01:07:12.000
dc.date.accessioned 2020-06-30T04:46:23Z
dc.date.available 2020-06-30T04:46:23Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 2003
dc.date.embargo 2018-09-12
dc.date.issued 2003-01-01
dc.description.abstract <p>We analyze an optimization-based approach called the NP-Filter for feature selection and show how the scalability of this method can be improved using random sampling of instances from the training data. The NP-Filter has attractive theoretical properties as the final solution quality can be quantified and it is flexible in terms of incorporating various feature evaluation methods. We show how the NP-Filter can automatically adjust to the randomness that occurs when a sample of training instances is used, and present numerical results that illustrate both this key result and the scalability improvement that are obtained.</p>
dc.description.comments <p>This is a proceeding published as Yang, Jaekyung, and Sigurdur Olafsson. "Scalable Optimization-Based Feature Selection Using Random Sampling." In IIE Annual Conference. Proceedings, p. 1. Institute of Industrial and Systems Engineers (IISE), 2003. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/imse_conf/141/
dc.identifier.articleid 1153
dc.identifier.contextkey 12830167
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath imse_conf/141
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/44216
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/imse_conf/141/0-IISE_Permission.pdf|||Fri Jan 14 20:14:10 UTC 2022
dc.source.bitstream archive/lib.dr.iastate.edu/imse_conf/141/2003_Olafsson_ScalableOptimization.pdf|||Fri Jan 14 20:14:12 UTC 2022
dc.subject.disciplines Operations Research, Systems Engineering and Industrial Engineering
dc.subject.keywords Feature Selection
dc.subject.keywords Scalability
dc.subject.keywords Data Mining
dc.subject.keywords Optimization
dc.subject.keywords Nested Partition
dc.title Scalable Optimization-Based Feature Selection Using Random Sampling
dc.type article
dc.type.genre conference
dspace.entity.type Publication
relation.isAuthorOfPublication 485e1458-0389-4fa4-bf89-a25dec27125d
relation.isOrgUnitOfPublication 51d8b1a0-5b93-4ee8-990a-a0e04d3501b1
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