Abstract: With the widespread use of deep learning models,people are more aware that the decision-making of model is a problem that needs to be solved urgently.Complex and difficult-to-interpret black-box models hinder the deployment of algorithms in actual scenarios.LIME is the most popular method of local interpretation,but the resulting perturbed data is unstable,leading to bias in the final explanation.To solve the above problems,local interpretable model-agnostic explanations based on active learning and rational quadratic kernel,ActiveLIME,is proposed,which makes the local interpretable model more faithful to the original classifier.After ActiveLIME generates the perturbed data,it samples the perturbation through the query strategy of active lear-ning,selects the perturbation with high uncertainty for training,and uses the local model with the highest accuracy in the iteration to generate explanations for the instances of interest.And for high-dimensional sparse samples that are prone to local overfitting,a rational quadratic kernel is introduced into model’s loss function to reduce overfitting.Experiments indicate that the proposed ActiveLIME has better local fidelity and quality of explanations than traditional local explanation algorithms.

Key words: Local explanation, Perturbation sampling, Query strategy of active learning, Rational quadratic kernel