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Feature-Weighted Counterfactual-Based Explanation for Bankruptcy Prediction

Title
Feature-Weighted Counterfactual-Based Explanation for Bankruptcy Prediction
Authors
Cho S.H.Shin K.-S.
Ewha Authors
신경식
SCOPUS Author ID
신경식scopus
Issue Date
2023
Journal Title
Expert Systems with Applications
ISSN
0957-4174JCR Link
Citation
Expert Systems with Applications vol. 216
Keywords
Bankruptcy predictionCounterfactual-based explanationExplainable artificial intelligence
Publisher
Elsevier Ltd
Indexed
SCIE; SCOPUS scopus
Document Type
Article
Abstract
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
DOI
10.1016/j.eswa.2022.119390
Appears in Collections:
경영대학 > 경영학전공 > Journal papers
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