A Generalized Weighted Loss for SVC and MLP
This work addresses incremental improvements in loss function design for binary classification and regression tasks, potentially benefiting practitioners in these areas.
The authors tackled the problem of standard loss functions in machine learning by introducing generalized weighted loss schemes for Support Vector Classification and Multi-layer Perceptron, resulting in error performance that is never worse and sometimes better than standard methods.
Usually standard algorithms employ a loss where each error is the mere absolute difference between the true value and the prediction, in case of a regression task. In the present, we introduce several error weighting schemes that are a generalization of the consolidated routine. We study both a binary classification model for Support Vector Classification and a regression net for Multi-layer Perceptron. Results proves that the error is never worse than the standard procedure and several times it is better.