  Training models on highly unbalanced data is admitted to be a challenging
task for machine learning algorithms. Current studies on deep learning mainly
focus on data sets with balanced class labels or unbalanced data, but with
massive amount of samples available, like in speech recognition. However, the
capacities of deep learning on imbalanced data with little samples is not
deeply investigated in literature, while it is a very common application
context in numerous industries. To contribute to fill this gap, this paper
compares the performances of several popular machine learning algorithms
previously applied with success to unbalanced data set with deep learning
algorithms. We conduct those experiments on a highly unbalanced data set, used
for credit scoring. We evaluate various configuration including neural network
optimization techniques and try to determine their capacities when they operate
with imbalanced corpora.
