In this paper, we propose a fuzzy adaptive loss function for enhancing deep learning performance in classification tasks. Specifically, we redefine the cross-entropy loss to effectively address class-level noise conditions, including the challenging problem of class imbalance. Our approach introduces aggregation operators, leveraging the power of fuzzy logic to improve classification accuracy. The rationale behind our proposed method lies in the iterative up-weighting of class-level components within the loss function, focusing on those with larger errors. To achieve this, we employ the ordered weighted average (OWA) operator and combine it with an adaptive scheme for gradient-based learning. Through extensive experimentation, our method outperforms other commonly used loss functions, such as the standard cross-entropy or focal loss, across various binary and multiclass classification tasks. Furthermore, we explore the influence of hyperparameters associated with the OWA operators and present a default configuration that performs well across different experimental settings.

翻译：本文提出一种模糊自适应损失函数，用于提升深度学习在分类任务中的性能。具体而言，我们重新定义了交叉熵损失函数，以有效应对类别级噪声条件，包括具有挑战性的类别不平衡问题。该方法引入聚合算子，借助模糊逻辑的力量提高分类精度。所提方法的核心思想在于，在损失函数中对类别级分量进行迭代加权，重点关注具有较大误差的分量。为此，我们采用有序加权平均（OWA）算子，并将其与梯度学习的自适应方案相结合。通过大量实验，该方法在多种二分类和多分类任务中优于其他常用损失函数，如标准交叉熵损失或焦点损失。此外，我们探讨了与OWA算子相关的超参数影响，并提出了一种在不同实验设置下均表现良好的默认配置。