  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.
