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• W2285943661 abstract "Class imbalance is a common problem when dealing with real life classification problems. Support Vector Machines, a well-known family of learning algorithms is prone to produce a skewed classification function when the training patterns of one class are outnumbered by the quantity of samples of the other classes. In this work we study methods available in the literature to alleviate this skewness, concretely those relating the technique of using conformal transformations of the kernel function to adapt the feature space in a data-dependent way. After that, we propose two new methods (MDT and TSVT) as an addition to the previous ones, and compare the obtained results between both some of the reviewed and proposed methods." @default.
• W2285943661 created "2016-06-24" @default.
• W2285943661 creator A5048484256 @default.
• W2285943661 date "2008-01-01" @default.
• W2285943661 modified "2023-09-24" @default.
• W2285943661 title "SVM imbalance correction by conformal kernel transformations" @default.
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• W2285943661 hasPublicationYear "2008" @default.
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