  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.
