Feature-Weighted Counterfactual-Based Explanation for Bankruptcy Prediction
- 주제(키워드) Bankruptcy prediction , Counterfactual-based explanation , Explainable artificial intelligence
- 등재 SCIE, SCOPUS
- 발행기관 Elsevier Ltd
- 발행년도 2023
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000203373
- 본문언어 영어
- Published As https://doi.org/10.1016/j.eswa.2022.119390
초록/요약
In recent years, there have been many studies on the application and implementation of machine learning techniques in the financial domain. Implementation of such state-of-the-art models inevitably requires interpretability for users to understand the result and trust. However, as most of the machine learning methods are “black-box,” explainable AI, which aims to provide explanations to users, has become an important research issue. This paper focuses on explanation by counterfactual example for a bankruptcy-prediction model. Counterfactual-based explanation offers an alternative case for users in order for them to have a desired output from the model. This paper proposes a genetic algorithm (GA)-based counterfactual generation algorithm using feature importance whilst taking other key factors into account. Feature importance was derived from a prediction model, and key factors for counterfactuals include closeness to the original dataset and sparsity. The proposed method presented advantages over the nearest contrastive sample and a simple counterfactual generation algorithm in the experiment. Also, it provides relevant and compact explanations to enhance the interpretability of the bankruptcy prediction model. © 2022
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