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- W68908521 abstract "The multi-class imbalance problem in supervised pattern recognition methods is receiving growing attention. Imbalanced datasets means that some classes are represented by a large number of samples while the others classes only contain a few. In real-world applications, imbalanced training sets may produce an important deterioration of the classifier performance when neural networks are applied in the classes less represented. In this paper we propose training cost-sentitive neural networks with editing techniques for handling the class imbalance problem on multi-class datasets. The aim is to remove majority samples while compensating the class imbalance during the training process. Experiments with real data sets demonstrate the effectiveness of the strategy here proposed." @default.
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- W68908521 date "2010-01-01" @default.
- W68908521 modified "2023-10-05" @default.
- W68908521 title "Cost-Sensitive Neural Networks and Editing Techniques for Imbalance Problems" @default.
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- W68908521 doi "https://doi.org/10.1007/978-3-642-15992-3_20" @default.
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