We present SHAPNN, a novel deep tabular data modeling architecture designed
for supervised learning. Our approach leverages Shapley values, a
well-established technique for explaining black-box models. Our neural network
is trained using standard backward propagation optimization methods, and is
regularized with realtime estimated Shapley values. Our method offers several
advantages, including the ability to provide valid explanations with no
computational overhead for data instances and datasets. Additionally,
prediction with explanation serves as a regularizer, which improves the model's
performance. Moreover, the regularized prediction enhances the model's
capability for continual learning. We evaluate our method on various publicly
available datasets and compare it with state-of-the-art deep neural network
models, demonstrating the superior performance of SHAPNN in terms of AUROC,
transparency, as well as robustness to streaming data.

SHAPNN 是一种新颖的深度表格数据建模架构，通过利用 Shapley 值，一种解释黑盒模型的常用技术，我们的神经网络使用标准的反向传播优化方法进行训练，并利用实时估计的 Shapley 值进行正则化。我们的方法具有多个优点，包括可以为数据实例和数据集提供有效的解释而无需计算开销，而且带解释的预测还可以作为正则化器，提高模型性能，增强模型的持续学习能力。我们在各种公开可用的数据集上评估了我们的方法，并与最先进的深度神经网络模型进行比较，展示了 SHAPNN 在 AUROC、透明度和对流数据的鲁棒性方面的卓越性能。