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- W122912556 abstract "AbstractWe present a framework to address the imbalanced data problem using semi-supervised learning. Specifically, from a supervised problem, we create a semi-supervised problem and then use a semi-supervised learning method to identify the most relevant instances to establish a well-defined training set. We present extensive experimental results, which demonstrate that the proposed framework significantly outperforms all other sampling algorithms in 67% of the cases across three different classifiers and ranks second best for the remaining 33% of the cases.KeywordsUnlabeled DataMinority ClassClass ImbalanceImbalanced DataImbalanced DatasetThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W122912556 date "2014-01-01" @default.
- W122912556 modified "2023-09-27" @default.
- W122912556 title "Empowering Imbalanced Data in Supervised Learning: A Semi-supervised Learning Approach" @default.
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- W122912556 doi "https://doi.org/10.1007/978-3-319-11179-7_66" @default.
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