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A comprehensive theoretical framework for the optimization of neural networks classification performance with respect to weighted metrics
- Publication Year :
- 2023
-
Abstract
- In many contexts, customized and weighted classification scores are designed in order to evaluate the goodness of the predictions carried out by neural networks. However, there exists a discrepancy between the maximization of such scores and the minimization of the loss function in the training phase. In this paper, we provide a complete theoretical setting that formalizes weighted classification metrics and then allows the construction of losses that drive the model to optimize these metrics of interest. After a detailed theoretical analysis, we show that our framework includes as particular instances well-established approaches such as classical cost-sensitive learning, weighted cross entropy loss functions and value-weighted skill scores.
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2305.13472
- Document Type :
- Working Paper