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arXiv · 2305.13472

A comprehensive theoretical framework for the optimization of neural networks classification performance with respect to weighted metrics

Francesco Marchetti·Sabrina Guastavino·Cristina Campi·Federico Benvenuto·Michele Piana
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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.

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Francesco Marchetti, Sabrina Guastavino, Cristina Campi, Federico Benvenuto, Michele Piana. 2023-05-22. A comprehensive theoretical framework for the optimization of neural networks classification performance with respect to weighted metrics. https://arxiv.org/abs/2305.13472

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