A qualitatively analyzable two-stage ensemble model based on machine learning for credit risk early warning: Evidence from Chinese manufacturing companies-Reference-Cited by-同舟云学术

A qualitatively analyzable two-stage ensemble model based on machine learning for credit risk early warning: Evidence from Chinese manufacturing companies

Published:2023-05 Issue:3 Volume:60 Page:103267
ISSN:0306-4573
Container-title:Information Processing & Management
language:en
Short-container-title:Information Processing & Management

Author:

Wang Lu,Zhang Wenyao

Funder

Ministry of Education of the People's Republic of China

China Scholarship Council

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Library and Information Sciences,Management Science and Operations Research,Computer Science Applications,Media Technology,Information Systems

Reference53 articles.

1. Explainability: Relevance based dynamic deep learning algorithm for fault detection and diagnosis in chemical processes;Agarwal;Computers & Chemical Engineering,2021

2. Improvement of e-polylysine production by marine bacterium Bacillus licheniformis using artificial neural network modeling and particle swarm optimization technique;Bhattacharya;Biochemical Engineering Journal,2017

3. An experimental comparison of classification algorithms for imbalanced credit scoring data sets;Brown;Expert Systems with Applications,2012

4. Explainable machine learning in credit risk management;Bussmann;Computational Economics,2021

5. Combination of feature selection approaches with SVM in credit scoring;Chen;Expert Systems with Applications,2010

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Forecasting time to risk based on multi-party data: An explainable privacy-preserving decentralized survival analysis method;Information Processing & Management;2025-01

2. A novel URP-CNN model for bond credit risk evaluation of Chinese listed companies;Expert Systems with Applications;2024-12

3. Sequential three-way decision with automatic threshold learning for credit risk prediction;Applied Soft Computing;2024-11

4. Corporate financial distress prediction using the risk-related information content of annual reports;Information Processing & Management;2024-09

5. Combining travel behavior in metro passenger flow prediction: A smart explainable Stacking-Catboost algorithm;Information Processing & Management;2024-07