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New Oversampling Approaches Based on Polynomial Fitting for Imbalanced Data Sets
2008
2008 The Eighth IAPR International Workshop on Document Analysis Systems
In classification tasks, class-modular strategy has been widely used. It has outperformed classical strategy for pattern classification task in many applications [1] . However, in some modular architecture, such as one against all in support vector machines classifier, the training dataset for one class risks to heavily outnumber the other classes. In this challenging situation, the trained classifier will accurately classify the majority class; nevertheless, it marginalizes the minority class.
doi:10.1109/das.2008.74
dblp:conf/das/GazzahA08
fatcat:rkogfrydingllliujt7ki2nc5y