系统管理学报2026，Vol.35Issue(2)：452-461,10.DOI:10.3969/j.issn2097-4558.2026.02.011
基于D-S证据融合的可解释多分类财务危机预警模型
An Interpretable Multi-Class Financial Crisis Early Warning Model Based on D-S Evidence Fusion
摘要
Abstract
To address the limitation of traditional binary financial distress prediction models in providing fine-grained,tiered early warnings,this paper constructs an interpretable multi-class financial crisis early warning model based on the fusion of financial and non-financial information.First,management discussion and analysis(MD&A)tone information is incorporated to enrich data sources for small and medium-sized enterprises(SMEs).Subsequently,random forest(RF),light gradient boosting machine(LightGBM),and support vector machine(SVM)models are utilized to predict the financial performance of SMEs,which are then further integrated using an improved Dempster-Shafer(D-S)evidence theory.Finally,the SHapley Additive exPlanations(SHAP)is introduced to facilitate interpretable analysis.The results show that the information-fusion model exhibits a 1.3％improvement in the F1 score compared with the best-performing base classifier,effectively avoiding prediction"disaster points."The model also identifies key early warning indicators such as the debt-to-asset ratio,undistributed earnings per share,and return on equity.Overall,the proposed model achieves more accurate financial crisis classification and more stable predictive performance,thereby providing a novel perspective for financial crisis early warning research on SMEs.关键词
多分类/财务危机预警/信息融合/SHAP/决策支持Key words
multiple-class classification/financial crisis early warning/information fusion/SHapley Additive exPlanations(SHAP)/decision support分类
管理科学引用本文复制引用
宋媚,李佳蔚,高峰,洪维强..基于D-S证据融合的可解释多分类财务危机预警模型[J].系统管理学报,2026,35(2):452-461,10.基金项目
国家自然科学基金资助项目(71503108,62077029) （71503108,62077029）
江苏师范大学研究生科研与实践创新计划项目(2024XKT2647) （2024XKT2647）
