Classification of imbalanced remote-sensing data by neural networks
Pattern Recognition Letters - special issue on pattern recognition in practice V
Robust Classification for Imprecise Environments
Machine Learning
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
Instance weighting versus threshold adjusting for cost-sensitive classification
Knowledge and Information Systems
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
EUS SVMs: ensemble of under-sampled SVMs for data imbalance problems
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Improving the error backpropagation algorithm with a modified error function
IEEE Transactions on Neural Networks
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
Pattern Recognition Letters
Boosting weighted ELM for imbalanced learning
Neurocomputing
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Classification of imbalanced data is pervasive but it is a difficult problem to solve. In order to improve the classification of imbalanced data, this letter proposes a new error function for the error back-propagation algorithm of multilayer perceptrons. The error function intensifies weight-updating for the minority class and weakens weight-updating for the majority class. We verify the effectiveness of the proposed method through simulations on mammography and thyroid data sets.