  The minimal feature removal problem in the post-hoc explanation area aims to
identify the minimal feature set (MFS). Prior studies using the greedy
algorithm to calculate the minimal feature set lack the exploration of feature
interactions under a monotonic assumption which cannot be satisfied in general
scenarios. In order to address the above limitations, we propose a Cooperative
Integrated Dynamic Refining method (CIDR) to efficiently discover minimal
feature sets. Specifically, we design Cooperative Integrated Gradients (CIG) to
detect interactions between features. By incorporating CIG and characteristics
of the minimal feature set, we transform the minimal feature removal problem
into a knapsack problem. Additionally, we devise an auxiliary Minimal Feature
Refinement algorithm to determine the minimal feature set from numerous
candidate sets. To the best of our knowledge, our work is the first to address
the minimal feature removal problem in the field of natural language
processing. Extensive experiments demonstrate that CIDR is capable of tracing
representative minimal feature sets with improved interpretability across
various models and datasets.
