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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Financial distress prediction using a corrected feature selection measure and gradient boosted decision tree
Hongyi Qian, Baohui Wang, Minghe Yuan, et al.
Expert Systems with Applications (2021) Vol. 190, pp. 116202-116202
Closed Access | Times Cited: 85

Showing 1-25 of 85 citing articles:

An innovative interpretable combined learning model for wind speed forecasting
Pei Du, Dongchuan Yang, Yanzhao Li, et al.
Applied Energy (2024) Vol. 358, pp. 122553-122553
Closed Access | Times Cited: 19
Predicting financial distress using multimodal data: An attentive and regularized deep learning method
Wanliu Che, Zhao Wang, Cuiqing Jiang, et al.
Information Processing & Management (2024) Vol. 61, Iss. 4, pp. 103703-103703
Open Access | Times Cited: 15
A robust and interpretable ensemble machine learning model for predicting healthcare insurance fraud
Zeyu Wang, Xiaofang Chen, Yiwei Wu, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 2
A novel ensemble feature selection method by integrating multiple ranking information combined with an SVM ensemble model for enterprise credit risk prediction in the supply chain
Gang Yao, Xiaojian Hu, Guanxiong Wang
Expert Systems with Applications (2022) Vol. 200, pp. 117002-117002
Closed Access | Times Cited: 55
Research on prediction of multi-class theft crimes by an optimized decomposition and fusion method based on XGBoost
Zhongzhen Yan, Hao Chen, Xinhua Dong, et al.
Expert Systems with Applications (2022) Vol. 207, pp. 117943-117943
Closed Access | Times Cited: 40
The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China
Shusheng Ding, Tianxiang Cui, Anthony Graham Bellotti, et al.
International Review of Financial Analysis (2023) Vol. 90, pp. 102851-102851
Closed Access | Times Cited: 26
Artificial Intelligence in Accounting and Finance: Challenges and Opportunities
Ziwei Yi, Xinwei Cao, Zuyan Chen, et al.
IEEE Access (2023) Vol. 11, pp. 129100-129123
Open Access | Times Cited: 26
Addressing Bias in Feature Importance: A Hybrid Approach for Risk Prediction in Prognostic Survival Models
Yoshiyasu Takefuji
JCO Precision Oncology (2025), Iss. 9
Closed Access | Times Cited: 1
Impacts of crisis on SME bankruptcy prediction models’ performance
Mário Papík, Lenka Papíková
Expert Systems with Applications (2022) Vol. 214, pp. 119072-119072
Closed Access | Times Cited: 33
Systematic Review of Financial Distress Identification using Artificial Intelligence Methods
Dovilė Kuizinienė, Tomas Krilavičius, Robertas Damaševičius, et al.
Applied Artificial Intelligence (2022) Vol. 36, Iss. 1
Open Access | Times Cited: 31
The evaluation of bankruptcy prediction models based on socio-economic costs
Jelena Radovanovic, Christian Haas
Expert Systems with Applications (2023) Vol. 227, pp. 120275-120275
Open Access | Times Cited: 20
Machine learning-based predictive modelling for the enhancement of wine quality
Khushboo Jain, Keshav Kaushik, Sachin Kumar Gupta, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 20
Soft reordering one-dimensional convolutional neural network for credit scoring
Hongyi Qian, Ping Ma, Songfeng Gao, et al.
Knowledge-Based Systems (2023) Vol. 266, pp. 110414-110414
Closed Access | Times Cited: 19
A case-based reasoning driven ensemble learning paradigm for financial distress prediction with missing data
Lean Yu, Mengxin Li
Applied Soft Computing (2023) Vol. 137, pp. 110163-110163
Closed Access | Times Cited: 19
A two-stage case-based reasoning driven classification paradigm for financial distress prediction with missing and imbalanced data
Lean Yu, Mengxin Li, Xiaojun Liu
Expert Systems with Applications (2024) Vol. 249, pp. 123745-123745
Closed Access | Times Cited: 7
Prediction of financial distress of companies with artificial neural networks and decision trees models
Nezir Aydın, Nida Sahin, Muhammet Deveci, et al.
Machine Learning with Applications (2022) Vol. 10, pp. 100432-100432
Open Access | Times Cited: 25
Financial risk prediction in supply chain finance based on buyer transaction behavior
Zelong Yi, Zhuomin Liang, Tongtong Xie, et al.
Decision Support Systems (2023) Vol. 170, pp. 113964-113964
Closed Access | Times Cited: 15
Bankruptcy prediction with low-quality financial information
Eduardo da Silva Mattos, Dennis Shasha
Expert Systems with Applications (2023) Vol. 237, pp. 121418-121418
Closed Access | Times Cited: 14
Credit default prediction of Chinese real estate listed companies based on explainable machine learning
Yuanyuan Ma, Pingping Zhang, Shaodong Duan, et al.
Finance research letters (2023) Vol. 58, pp. 104305-104305
Closed Access | Times Cited: 13
Machine Learning for Credit Risk Prediction: A Systematic Literature Review
Jomark Pablo Noriega, Luis Rivera, José Herrera
Data (2023) Vol. 8, Iss. 11, pp. 169-169
Open Access | Times Cited: 13
Machine learning techniques in bankruptcy prediction: A systematic literature review
Απόστολος Δασίλας, Anna Rigani
Expert Systems with Applications (2024) Vol. 255, pp. 124761-124761
Closed Access | Times Cited: 5
A Review on Intelligent Recognition with Logging Data: Tasks, Current Status and Challenges
Xinyi Zhu, Hongbing Zhang, Quan Ren, et al.
Surveys in Geophysics (2024) Vol. 45, Iss. 5, pp. 1493-1526
Closed Access | Times Cited: 5
Predicting financial distress in high-dimensional imbalanced datasets: a multi-heterogeneous self-paced ensemble learning framework
Ruize Gao, Shaoze Cui, Yu Wang, et al.
Financial Innovation (2025) Vol. 11, Iss. 1
Open Access
What makes companies zombie? Detecting the most important zombification feature using tree-based machine learning
Rayenda Khresna Brahmana
Expert Systems with Applications (2025), pp. 126538-126538
Open Access
A hybrid clustering and boosting tree feature selection (CBTFS) method for credit risk assessment with high-dimensionality
Jianxin Zhu, Xiong Wu, Lean Yu, et al.
Technological and Economic Development of Economy (2025), pp. 1-33
Open Access
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