  Learning an explainable classifier often results in low accuracy model or
ends up with a huge rule set, while learning a deep model is usually more
capable of handling noisy data at scale, but with the cost of hard to explain
the result and weak at generalization. To mitigate this gap, we propose an
end-to-end deep explainable learning approach that combines the advantage of
deep model in noise handling and expert rule-based interpretability.
Specifically, we propose to learn a deep data assessing model which models the
data as a graph to represent the correlations among different observations,
whose output will be used to extract key data features. The key features are
then fed into a rule network constructed following predefined noisy expert
rules with trainable parameters. As these models are correlated, we propose an
end-to-end training framework, utilizing the rule classification loss to
optimize the rule learning model and data assessing model at the same time. As
the rule-based computation is none-differentiable, we propose a gradient
linking search module to carry the gradient information from the rule learning
model to the data assessing model. The proposed method is tested in an industry
production system, showing comparable prediction accuracy, much higher
generalization stability and better interpretability when compared with a
decent deep ensemble baseline, and shows much better fitting power than pure
rule-based approach.
