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A surrogate loss function for optimization of F_β score in binary classification with imbalanced data
[article]
2021
arXiv
pre-print
The F_β score is a commonly used measure of classification performance, which plays crucial roles in classification tasks with imbalanced data sets. However, the F_β score cannot be used as a loss function by gradient-based learning algorithms for optimizing neural network parameters due to its non-differentiability. On the other hand, commonly used loss functions such as the binary cross-entropy (BCE) loss are not directly related to performance measures such as the F_β score, so that neural
arXiv:2104.01459v1
fatcat:jspig4w6bfhmpmwrw47t45we3q