  Apart from the high accuracy of machine learning models, what interests many
researchers in real-life problems (e.g., fraud detection, credit scoring) is to
find hidden patterns in data; particularly when dealing with their challenging
imbalanced characteristics. Interpretability is also a key requirement that
needs to accompany the used machine learning model. In this concern, often,
intrinsically interpretable models are preferred to complex ones, which are in
most cases black-box models. Also, linear models are used in some high-risk
fields to handle tabular data, even if performance must be sacrificed. In this
paper, we introduce Self-Reinforcement Attention (SRA), a novel attention
mechanism that provides a relevance of features as a weight vector which is
used to learn an intelligible representation. This weight is then used to
reinforce or reduce some components of the raw input through element-wise
vector multiplication. Our results on synthetic and real-world imbalanced data
show that our proposed SRA block is effective in end-to-end combination with
baseline models.
