Cost-sensitive learning methods for imbalanced data

By Nguyen Thai-Nghe, Zeno Gantner, Lars Schmidt-Thieme. IEEE International Joint Conference on Neural Network, 2010

342 citations recorded by the source corpus.

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Ten simple rules for predictive modeling of individual differences in neuroimaging (2019, background): “strategy for overcoming this classification bias is to ‘down-weight’ or penalize classifications for the larger group (in this case, the placebo group) (Thai-Nghe et al., 2010).”
Trustworthy AI: From Principles to Practices (2021, background): “, for the screening of highly contagious and severe diseases, false negatives can be costlier than false positives; see cost-sensitive learning [321]).”
Training deep neural networks on imbalanced data sets (2016, background): “Be contrasted with sampling methods, cost sensitive learning methods solve data imbalance problem based on the consideration of the cost associated with misclassifying samples [11].”
Collective Opinion Spam Detection: Bridging Review Networks and Metadata (2015, methodology): “Therefore, one needs to either use cost-sensitive methods [4, 25] or deploy under/over-sampling techniques to balance the training data before learning [2].”