Feature-weighted Counterfactual Explanation Method:A Case Study in Credit Risk Control Scenarios
The application of machine learning technology in the financial field is becoming more and more prevalent,and provi-ding interpretable machine learning methods to users has become an important research topic.In recent years,counterfactual ex-planation has attracted widespread attention,which improves the interpretability of machine learning models by providing pertur-bation vectors to change the predicted results obtained by classifiers.However,existing methods face feasibility and operability is-sues in generating counterfactual instances.This paper proposes a new counterfactual explanation framework that introduces the concept of feature-variable cost weight matrix,considering the ease of changing different feature variables to make the counterfac-tual results more realistic and feasible.At the same time,by predefining the feature-variable cost weight matrix by experts,a fea-sible method for calculating the cost weight of feature variables is pro posed,allowing users to make personalized adjustments ac-cording to actual situations.The defined objective function comprehensively considers three indicators:feature-weighted distance,sparsity,and proximity,ensuring the feasibility,simplicity,and closeness to the original sample set of counterfactual results.Ge-netic algorithms are used to solve the problem and generate the optimal action plan.Through experiments on real datasets,it is confirmed that our method can generate feasible and actionable counterfactual instances compared to existing counterfactual me-thods.
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