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

IF 1.3 4区数学 Q2 MATHEMATICS, APPLIED

Optimization Letters Pub Date : 2024-04-25 DOI:10.1007/s11590-024-02112-1

Francesco Marchetti, Sabrina Guastavino, Cristina Campi, Federico Benvenuto, Michele Piana

引用次数: 0

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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根据加权指标优化神经网络分类性能的综合理论框架

在许多情况下，设计定制的加权分类分数是为了评估神经网络预测的好坏。然而，这些分数的最大化与训练阶段损失函数的最小化之间存在差异。在本文中，我们提供了一个完整的理论环境，将加权分类指标形式化，然后构建损失函数，驱动模型优化这些相关指标。经过详细的理论分析，我们表明，我们的框架包括了一些成熟的方法，如经典的成本敏感学习、加权交叉熵损失函数和价值加权技能分数。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Optimization Letters 管理科学-应用数学

CiteScore

3.40

自引率

6.20%

发文量

116

审稿时长

9 months

期刊介绍： Optimization Letters is an international journal covering all aspects of optimization, including theory, algorithms, computational studies, and applications, and providing an outlet for rapid publication of short communications in the field. Originality, significance, quality and clarity are the essential criteria for choosing the material to be published. Optimization Letters has been expanding in all directions at an astonishing rate during the last few decades. New algorithmic and theoretical techniques have been developed, the diffusion into other disciplines has proceeded at a rapid pace, and our knowledge of all aspects of the field has grown even more profound. At the same time one of the most striking trends in optimization is the constantly increasing interdisciplinary nature of the field. Optimization Letters aims to communicate in a timely fashion all recent developments in optimization with concise short articles (limited to a total of ten journal pages). Such concise articles will be easily accessible by readers working in any aspects of optimization and wish to be informed of recent developments.