  Many evaluation metrics can be used to assess the performance of models in
binary classification tasks. However, most of them are derived from a confusion
matrix in a non-differentiable form, making it very difficult to generate a
differentiable loss function that could directly optimize them. The lack of
solutions to bridge this challenge not only hinders our ability to solve
difficult tasks, such as imbalanced learning, but also requires the deployment
of computationally expensive hyperparameter search processes in model
selection. In this paper, we propose a general-purpose approach that transforms
any confusion matrix-based metric into a loss function, \textit{AnyLoss}, that
is available in optimization processes. To this end, we use an approximation
function to make a confusion matrix represented in a differentiable form, and
this approach enables any confusion matrix-based metric to be directly used as
a loss function. The mechanism of the approximation function is provided to
ensure its operability and the differentiability of our loss functions is
proved by suggesting their derivatives. We conduct extensive experiments under
diverse neural networks with many datasets, and we demonstrate their general
availability to target any confusion matrix-based metrics. Our method,
especially, shows outstanding achievements in dealing with imbalanced datasets,
and its competitive learning speed, compared to multiple baseline models,
underscores its efficiency.
