  In some industrial applications such as fraud detection, the performance of
common supervision techniques may be affected by the poor quality of the
available labels : in actual operational use-cases, these labels may be weak in
quantity, quality or trustworthiness. We propose a benchmark to evaluate the
natural robustness of different algorithms taken from various paradigms on
artificially corrupted datasets, with a focus on noisy labels. This paper
studies the intrinsic robustness of some leading classifiers. The algorithms
under scrutiny include SVM, logistic regression, random forests, XGBoost,
Khiops. Furthermore, building on results from recent literature, the study is
supplemented with an investigation into the opportunity to enhance some
algorithms with symmetric loss functions.
