To address the issues of stability and fidelity in interpretable learning, a
novel interpretable methodology, ensemble interpretation, is presented in this
paper which integrates multi-perspective explanation of various interpretation
methods. On one hand, we define a unified paradigm to describe the common
mechanism of different interpretation methods, and then integrate the multiple
interpretation results to achieve more stable explanation. On the other hand, a
supervised evaluation method based on prior knowledge is proposed to evaluate
the explaining performance of an interpretation method. The experiment results
show that the ensemble interpretation is more stable and more consistent with
human experience and cognition. As an application, we use the ensemble
interpretation for feature selection, and then the generalization performance
of the corresponding learning model is significantly improved.

通过集成多角度解释的各种解释方法，本研究提出了一种新的适用于可解释性学习的方法，即集成解释方法。实验结果显示，集成解释方法更稳定，并且更符合人类经验和认知。作为应用，我们将集成解释方法用于特征选择，从而显著改善了相应学习模型的泛化性能。