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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:

An improved boosting based on feature selection for corporate bankruptcy prediction
Gang Wang, Jian Ma, Shanlin Yang
Expert Systems with Applications (2013) Vol. 41, Iss. 5, pp. 2353-2361
Closed Access | Times Cited: 168

Showing 1-25 of 168 citing articles:

Machine learning models and bankruptcy prediction
Flávio Barboza, Herbert Kimura, Edward I. Altman
Expert Systems with Applications (2017) Vol. 83, pp. 405-417
Closed Access | Times Cited: 692
Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach
You Zhu, Li Zhou, Chi Xie, et al.
International Journal of Production Economics (2019) Vol. 211, pp. 22-33
Open Access | Times Cited: 295
Systematic review of bankruptcy prediction models: Towards a framework for tool selection
Hafiz Alaka, Lukumon O. Oyedele, Hakeem A. Owolabi, et al.
Expert Systems with Applications (2017) Vol. 94, pp. 164-184
Open Access | Times Cited: 272
Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks
Félix J. López‐Iturriaga, Iván Pastor Sanz
Expert Systems with Applications (2014) Vol. 42, Iss. 6, pp. 2857-2869
Closed Access | Times Cited: 188
A benchmark of machine learning approaches for credit score prediction
Vincenzo Moscato, Antonio Picariello, Giancarlo Sperlì
Expert Systems with Applications (2020) Vol. 165, pp. 113986-113986
Closed Access | Times Cited: 173
Corporate bankruptcy prediction: a high dimensional analysis
Stewart Jones
Review of Accounting Studies (2017) Vol. 22, Iss. 3, pp. 1366-1422
Closed Access | Times Cited: 172
Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering
Sami Ben Jabeur, Nicolae Stef, Pedro Carmona
Computational Economics (2022) Vol. 61, Iss. 2, pp. 715-741
Closed Access | Times Cited: 101
Survey, classification and critical analysis of the literature on corporate bankruptcy and financial distress prediction
Jinxian Zhao, Jamal Ouenniche, Johannes De Smedt
Machine Learning with Applications (2024) Vol. 15, pp. 100527-100527
Open Access | Times Cited: 16
Financial crisis prediction model using ant colony optimization
J. Uthayakumar, Noura Metawa, K. Shankar, et al.
International Journal of Information Management (2018) Vol. 50, pp. 538-556
Closed Access | Times Cited: 125
Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios
Hong Hanh Le, Jean-Laurent Viviani
Research in International Business and Finance (2017) Vol. 44, pp. 16-25
Closed Access | Times Cited: 122
Hybrid genetic algorithm and fuzzy clustering for bankruptcy prediction
Chih-Hsun Chou, Su-Chen Hsieh, Chui-Jie Qiu
Applied Soft Computing (2017) Vol. 56, pp. 298-316
Closed Access | Times Cited: 119
Exploring the synergetic effects of sample types on the performance of ensembles for credit risk and corporate bankruptcy prediction
Vicente García, Ana I. Marqués, J. Salvador Sánchez
Information Fusion (2018) Vol. 47, pp. 88-101
Open Access | Times Cited: 117
Data analytic approach for bankruptcy prediction
Hyunwoo Son, Chongseok Hyun, Dinh‐Van Phan, et al.
Expert Systems with Applications (2019) Vol. 138, pp. 112816-112816
Closed Access | Times Cited: 116
A new perspective of performance comparison among machine learning algorithms for financial distress prediction
Yu‐Pei Huang, Meng-Feng Yen
Applied Soft Computing (2019) Vol. 83, pp. 105663-105663
Closed Access | Times Cited: 114
Enhancing accuracy and interpretability of ensemble strategies in credit risk assessment. A correlated-adjusted decision forest proposal
Raquel Flórez-López, Juan Manuel Ramón-Jerónimo
Expert Systems with Applications (2015) Vol. 42, Iss. 13, pp. 5737-5753
Closed Access | Times Cited: 98
A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks
S. Moradi, Farimah Mokhatab Rafiei
Financial Innovation (2019) Vol. 5, Iss. 1
Open Access | Times Cited: 91
A novel ensemble classification model based on neural networks and a classifier optimisation technique for imbalanced credit risk evaluation
Feng Shen, Xingchao Zhao, Zhiyong Li, et al.
Physica A Statistical Mechanics and its Applications (2019) Vol. 526, pp. 121073-121073
Closed Access | Times Cited: 91
Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE
Salima Smiti, Makram Soui
Information Systems Frontiers (2020) Vol. 22, Iss. 5, pp. 1067-1083
Closed Access | Times Cited: 89
Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study
Mohammad H. Nadimi-Shahraki, Shokooh Taghian, Seyedali Mirjalili, et al.
Mathematics (2022) Vol. 10, Iss. 11, pp. 1929-1929
Open Access | Times Cited: 60
A Novel CREDIT MODEL RISK MEASURE: Do more Data lead to lower model risk?
Valter T. Yoshida, Rafael Felipe Schiozer, Alan De Genaro, et al.
The Quarterly Review of Economics and Finance (2025), pp. 101960-101960
Closed Access | Times Cited: 1
Classifier Ensemble Design for Imbalanced Data Classification: A Hybrid Approach
Uma R. Salunkhe, Suresh N. Mali
Procedia Computer Science (2016) Vol. 85, pp. 725-732
Open Access | Times Cited: 65
A Survey on Machine Learning and Statistical Techniques in Bankruptcy Prediction
Sunitha Devi, Y. Radhika
International Journal of Machine Learning and Computing (2018) Vol. 8, Iss. 2, pp. 133-139
Open Access | Times Cited: 64
Failure prediction of Indian Banks using SMOTE, Lasso regression, bagging and boosting
Santosh Shrivastava, P. Jeyanthi, Sarbjit Singh
Cogent Economics & Finance (2020) Vol. 8, Iss. 1, pp. 1729569-1729569
Open Access | Times Cited: 62
A new random subspace method incorporating sentiment and textual information for financial distress prediction
Gang Wang, Gang Chen, Yan Chu
Electronic Commerce Research and Applications (2018) Vol. 29, pp. 30-49
Closed Access | Times Cited: 59
Big data analytics for default prediction using graph theory
Mustafa Yıldırım, Feyza Yıldırım Okay, Suat Özdemi̇r
Expert Systems with Applications (2021) Vol. 176, pp. 114840-114840
Closed Access | Times Cited: 53
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