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Improving financial bankruptcy prediction in a highly imbalanced class distribution using oversampling and ensemble learning: a case from the Spanish market
15 references·16 citing·31 second hop
Improving financial bankruptcy prediction in a highly imbalanced class distribution using oversampling and ensemble learning: a case from the Spanish market (2019)Improving financial bankruptcy prediction…Exploring the synergetic effects of sample types on the performance of ensembles for credit risk and corporate bankruptcy prediction (2018)Exploring the synergetic effe…Deep learning models for bankruptcy prediction using textual disclosures (2018)Deep learning models for bank…Bankruptcy prediction using imaged financial ratios and convolutional neural networks (2018)Bankruptcy prediction using i…An investigation of bankruptcy prediction in imbalanced datasets (2018)An investigation of bankruptc…Machine learning models and bankruptcy prediction (2017)Machine learning models and b…Exploring the behaviour of base classifiers in credit scoring ensembles (2012)Exploring the behaviour of ba…Bankruptcy prediction in firms with statistical and intelligent techniques and a comparison of evolutionary computation approaches (2011)Bankruptcy prediction in firm…Feature selection in bankruptcy prediction (2008)Feature selection in bankrupt…Predicting corporate bankruptcy: where we stand? 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References

  • 1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY14,869 cited
  • 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy6,005 cited
  • 1966Financial Ratios As Predictors of Failure4,753 cited
  • 2017Machine learning models and bankruptcy prediction952 cited
  • 2005Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters872 cited
  • 1994Bankruptcy prediction using neural networks722 cited
  • 2018Deep learning models for bankruptcy prediction using textual disclosures480 cited
  • 2006Predicting corporate bankruptcy: where we stand?402 cited
  • 2002A genetic algorithm application in bankruptcy prediction modeling386 cited
  • 2018Bankruptcy prediction using imaged financial ratios and convolutional neural networks322 cited
  • 2008Feature selection in bankruptcy prediction246 cited
  • 2018An investigation of bankruptcy prediction in imbalanced datasets185 cited
  • 2012Exploring the behaviour of base classifiers in credit scoring ensembles142 cited
  • 2018Exploring the synergetic effects of sample types on the performance of ensembles for credit risk and corporate bankruptcy prediction138 cited
  • 2011

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Bankruptcy prediction in firms with statistical and intelligent techniques and a comparison of evolutionary computation approaches
127 cited

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  • 2022Towards Explainable Machine Learning for Bank Churn Prediction Using Data Balancing and Ensemble-Based Methods53 cited
  • 2021Comparing the Performance of Deep Learning Methods to Predict Companies’ Financial Failure51 cited
  • 2024Corporate financial distress prediction using the risk-related information content of annual reports47 cited
  • 2021Company bankruptcy prediction framework based on the most influential features using XGBoost and stacking ensemble learning47 cited
  • 2021Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries41 cited
  • 2024Advancing financial resilience: A systematic review of default prediction models and future directions in credit risk management38 cited
  • 2020Cost-sensitive ensemble methods for bankruptcy prediction in a highly imbalanced data distribution: a real case from the Spanish market36 cited
  • 2022Anticipating financial distress of high‐tech startups in the European Union: A machine learning approach for imbalanced samples34 cited
  • 2022Bankruptcy prediction using synthetic sampling33 cited
  • 2022Cost-Sensitive Metaheuristic Optimization-Based Neural Network with Ensemble Learning for Financial Distress Prediction27 cited
  • 2021Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry26 cited
  • 2020Prediction of the Bankruptcy of Slovak Companies Using Neural Networks with SMOTE24 cited
  • 2024Future Trends and Opportunities in Machine Learning and Artificial Intelligence for Banking and Finance18 cited
  • 2025Machine Learning for Identifying Risk in Financial Statements: A Survey16 cited