Computer Science ›› 2022, Vol. 49 ›› Issue (9): 33-40.doi: 10.11896/jsjkx.220300158

• Database & Big Data & Data Science • Previous Articles     Next Articles

Generative Link Tree:A Counterfactual Explanation Generation Approach with High Data Fidelity

WANG Ming, WU Wen-fang, WANG Da-ling, FENG Shi, ZHANG Yi-fei   

  1. School of Computer Science and Engineering,Northeastern University,Shenyang 110169,China
  • Received:2022-03-16 Revised:2022-05-30 Online:2022-09-15 Published:2022-09-09
  • About author:WANG Ming,born in 1997,postgra-duate,is a student member of China Computer Federation.His main research interests include interpretable machine learning and counterfactual explanation.
    WANG Da-ling,born in 1962,Ph.D,professor,Ph.D supervisor,is a senior member of China Computer Federation.Her main research interests include social media processing,interpretable dialogue generation and sentiment analysis.
  • Supported by:
    National Natural Science Foundation of China(62172086,61872074).

Abstract: The super large data scale and complex structure of deep models show excellent performance in processing and application of Internet data,but reduce the interpretability of AI systems.Counterfactual Explanations(CE) has received much attention from researchers as a special kind of explanation approach in the field of interpretability research.Counterfactual Explanations can be regarded as a kind of generated data in addition to being an explanation.From the viewpoint of application,this paper proposes an approach for generating counterfactual explanations with high data fidelity,called generative link tree(GLT),which uses a partitioning strategy and a local greedy strategy to construct counterfactual explanations based on the cases appearing in the training data.Moreover,it summarizes the generation methods of counterfactual explanations and select popular datasets to verify the GLT method.In addition,the metric of “Data Fidelity (DF)” is proposed to evaluate the fidelity and potential application of the counterfactual explanation as data from an experimental perspective.Compared with the baseline method,the data fidelity of the counterfactual explanation generated by the GLT method is significantly higher than that of the counterfactual explanation gene-rated by the baseline model.

Key words: Interpretability, Filling type, Counterfactual explanations, Data fidelity

CLC Number: 

  • TP391
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