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dc.contributor.authorSong, Yale
dc.contributor.authorMorency, Louis-Philippe
dc.contributor.authorDavis, Randall
dc.date.accessioned2014-04-11T15:32:30Z
dc.date.available2014-04-11T15:32:30Z
dc.date.issued2013-04
dc.identifier.isbn978-1-4673-5546-9
dc.identifier.isbn978-1-4673-5545-2
dc.identifier.isbn978-1-4673-5544-5
dc.identifier.urihttp://hdl.handle.net/1721.1/86107
dc.description.abstractMany real-world face and gesture datasets are by nature imbalanced across classes. Conventional statistical learning models (e.g., SVM, HMM, CRY), however, are sensitive to imbalanced datasets. In this paper we show how an imbalanced dataset affects the performance of a standard learning algorithm, and propose a distribution-sensitive prior to deal with the imbalanced data problem. This prior analyzes the training dataset before learning a model, and puts more weight on the samples from underrepresented classes, allowing all samples in the dataset to have a balanced impact in the learning process. We report on two empirical studies regarding learning with imbalanced data, using two publicly available recent gesture datasets, the Microsoft Research Cambridge-12 (MSRC-12) and NATOPS aircraft handling signals datasets. Experimental results show that learning from balanced data is important, and that the distribution-sensitive prior improves performance with imbalanced datasets.en_US
dc.description.sponsorshipUnited States. Office of Naval Research (Grant N000140910625)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (Grant IIS-1118018)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (Grant IIS-1018055)en_US
dc.description.sponsorshipUnited States. Army Research, Development, and Engineering Commanden_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/FG.2013.6553715en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT web domainen_US
dc.titleDistribution-sensitive learning for imbalanced datasetsen_US
dc.typeArticleen_US
dc.identifier.citationSong, Yale, Louis-Philippe Morency, and Randall Davis. “Distribution-Sensitive Learning for Imbalanced Datasets.” 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) (n.d.).en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorSong, Yaleen_US
dc.contributor.mitauthorDavis, Randallen_US
dc.relation.journalProceedings of the 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsSong, Yale; Morency, Louis-Philippe; Davis, Randallen_US
dc.identifier.orcidhttps://orcid.org/0000-0001-5232-7281
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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