Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 신경식 | * |
dc.date.accessioned | 2023-01-06T16:30:08Z | - |
dc.date.available | 2023-01-06T16:30:08Z | - |
dc.date.issued | 2023 | * |
dc.identifier.issn | 0957-4174 | * |
dc.identifier.other | OAK-32796 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/263070 | - |
dc.description.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 | * |
dc.language | English | * |
dc.publisher | Elsevier Ltd | * |
dc.subject | Bankruptcy prediction | * |
dc.subject | Counterfactual-based explanation | * |
dc.subject | Explainable artificial intelligence | * |
dc.title | Feature-Weighted Counterfactual-Based Explanation for Bankruptcy Prediction | * |
dc.type | Article | * |
dc.relation.volume | 216 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SCOPUS | * |
dc.relation.journaltitle | Expert Systems with Applications | * |
dc.identifier.doi | 10.1016/j.eswa.2022.119390 | * |
dc.identifier.scopusid | 2-s2.0-85144607145 | * |
dc.author.google | Cho S.H. | * |
dc.author.google | Shin K.-S. | * |
dc.contributor.scopusid | 신경식(56927436200) | * |
dc.date.modifydate | 20240118131805 | * |