We propose a graph-oriented attention-based explainability method for tabular
data. Tasks involving tabular data have been solved mostly using traditional
tree-based machine learning models which have the challenges of feature
selection and engineering. With that in mind, we consider a transformer
architecture for tabular data, which is amenable to explainability, and present
a novel way to leverage self-attention mechanism to provide explanations by
taking into account the attention matrices of all layers as a whole. The
matrices are mapped to a graph structure where groups of features correspond to
nodes and attention values to arcs. By finding the maximum probability paths in
the graph, we identify groups of features providing larger contributions to
explain the model's predictions. To assess the quality of multi-layer
attention-based explanations, we compare them with popular attention-,
gradient-, and perturbation-based explanability methods.

该研究提出了一种基于图的注意力机制解释方法用于表格数据，使用 Transformer 架构易于实现可解释性，通过对所有层的注意力矩阵进行建模，通过识别出影响模型预测的重要特征组，可以提供多层的基于注意力的解释。