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Feature-Weighted Counterfactual-Based Explanation for Bankruptcy Prediction.
- Source :
-
Expert Systems with Applications . Apr2023, Vol. 216, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- • Counterfactual example-based explanation. • Explainable bankruptcy prediction model. • Feature-weighted multi-objective counterfactuals. • GA-based counterfactual generation algorithm. 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09574174
- Volume :
- 216
- Database :
- Academic Search Index
- Journal :
- Expert Systems with Applications
- Publication Type :
- Academic Journal
- Accession number :
- 161363070
- Full Text :
- https://doi.org/10.1016/j.eswa.2022.119390