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- W1990975532 abstract "Manually labeled data-sets are vital to graph-based semisupervised learning. However, in the real world, labeled data-sets are often heavily imbalanced, and the classifiers trained on such skewed data tend to show poor performance for low-frequency classes. In this paper, we deal with an imbalanced data case of semisupervised learning and propose a novel label matrix normalization solution called LMN to tackle the general imbalance problem. Experiments over different data-sets reveal the effectiveness of the devised algorithm." @default.
- W1990975532 created "2016-06-24" @default.
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- W1990975532 date "2013-11-06" @default.
- W1990975532 modified "2023-09-23" @default.
- W1990975532 title "Label matrix normalization for semisupervised learning from imbalanced Data" @default.
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- W1990975532 doi "https://doi.org/10.1080/13614568.2013.846416" @default.
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