Hybrid classification approach for imbalanced datasets
dc.contributor.advisor | Sigurdur Olafsson | |
dc.contributor.author | Gao, Tianxiang | |
dc.contributor.department | Industrial and Manufacturing Systems Engineering | |
dc.date | 2018-08-11T12:45:05.000 | |
dc.date.accessioned | 2020-06-30T02:55:58Z | |
dc.date.available | 2020-06-30T02:55:58Z | |
dc.date.copyright | Thu Jan 01 00:00:00 UTC 2015 | |
dc.date.embargo | 2001-01-01 | |
dc.date.issued | 2015-01-01 | |
dc.description.abstract | <p>The research area of imbalanced dataset has been attracted increasing attention from both academic and industrial areas, because it poses a serious issues for so many supervised learning problems. Since the number of majority class dominates the number of minority class are from minority class, if training dataset includes all data in order to fit a classic classifier, the classifier tends to classify all data to majority class by ignoring minority data as noise. Thus, it is very significant to select appropriate training dataset in the prepossessing stage for classification of imbalanced dataset. We propose an combination approach of SMOTE (Synthetic Minority Over-sampling Technique) and instance selection approaches. The numeric results show that the proposed combination approach can help classifiers to achieve better performance.</p> | |
dc.format.mimetype | application/pdf | |
dc.identifier | archive/lib.dr.iastate.edu/etd/14331/ | |
dc.identifier.articleid | 5338 | |
dc.identifier.contextkey | 7896969 | |
dc.identifier.doi | https://doi.org/10.31274/etd-180810-3884 | |
dc.identifier.s3bucket | isulib-bepress-aws-west | |
dc.identifier.submissionpath | etd/14331 | |
dc.identifier.uri | https://dr.lib.iastate.edu/handle/20.500.12876/28516 | |
dc.language.iso | en | |
dc.source.bitstream | archive/lib.dr.iastate.edu/etd/14331/Gao_iastate_0097M_14987.pdf|||Fri Jan 14 20:18:34 UTC 2022 | |
dc.subject.disciplines | Industrial Engineering | |
dc.subject.keywords | Industrial Engineering | |
dc.title | Hybrid classification approach for imbalanced datasets | |
dc.type | article | |
dc.type.genre | thesis | |
dspace.entity.type | Publication | |
relation.isOrgUnitOfPublication | 51d8b1a0-5b93-4ee8-990a-a0e04d3501b1 | |
thesis.degree.level | thesis | |
thesis.degree.name | Master of Science |
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