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• W2395921206 abstract "The imbalanced nature of some real-world data is one of the current challenges for machine learning, giving rise to dierent approaches to handling it. However, preprocessing methods operate in the original in- put space, presenting distortions when combined with the kernel classiers, which make use of the feature space. This paper explores the notion of empirical feature space (a Euclidean space which is isomorphic to the fea- ture space) to develop a kernel-based synthetic over-sampling technique, which maintains the main properties of the kernel mapping. The proposal achieves better results than the same oversampling method applied to the original input space." @default.
• W2395921206 created "2016-06-24" @default.
• W2395921206 creator A5063964437 @default.
• W2395921206 creator A5065093980 @default.
• W2395921206 creator A5086980043 @default.
• W2395921206 date "2013-01-01" @default.
• W2395921206 modified "2023-10-18" @default.
• W2395921206 title "Synthetic over-sampling in the empirical feature space" @default.
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• W2395921206 hasPublicationYear "2013" @default.
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