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The data sampling effect on financial distress prediction by single and ensemble learning techniques
16 references·0 citing·40 second hopsampled
The data sampling effect on financial distress prediction by single and ensemble learning techniques (2021)The data sampling effect on financial dis…Improving financial bankruptcy prediction in a highly imbalanced class distribution using oversampling and ensemble learning: a case from the Spanish market (2019)Improving financial bankruptc…Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets (2019)Bankruptcy prediction for sma…A Hybrid Approach Using Oversampling Technique and Cost‐Sensitive Learning for Bankruptcy Prediction (2019)A Hybrid Approach Using Overs…A CEEMDAN and XGBOOST‐Based Approach to Forecast Crude Oil Prices (2019)A CEEMDAN and XGBOOST‐Based A…Exploring the synergetic effects of sample types on the performance of ensembles for credit risk and corporate bankruptcy prediction (2018)Exploring the synergetic effe…An investigation of bankruptcy prediction in imbalanced datasets (2018)An investigation of bankruptc…Integrated framework for profit-based feature selection and SVM classification in credit scoring (2017)Integrated framework for prof…Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study (2016)Financial ratios and corporat…A comparative study on base classifiers in ensemble methods for credit scoring (2016)A comparative study on base c…Financial credit risk assessment: a recent review (2015)Financial credit risk assessm…Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches (2013)Predicting financial distress…Performance of corporate bankruptcy prediction models on imbalanced dataset: The effect of sampling methods (2013)Performance of corporate bank…Machine Learning in Financial Crisis Prediction: A Survey (2011)Machine Learning in Financial…Forecasting business failure: The use of nearest-neighbour support vectors and correcting imbalanced samples – Evidence from the Chinese hotel industry (2011)Forecasting business failure:…Combining cluster analysis with classifier ensembles to predict financial distress (2011)Combining cluster analysis wi…Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review (2006)Bankruptcy prediction in bank…Credit rating analysis with support vector machines and neural networks: a market comparative study (2003)Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters (2005)A new hybrid ensemble credit scoring model based on classifiers consensus system approach (2016)Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring (2019)EEG-based emotion recognition using tunable Q wavelet transform and rotation forest ensemble classifier (2021)Financial Ratios As Predictors of Failure (1966)FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY (1968)Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks (2014)Classifiers consensus system approach for credit scoring (2016)Financial Ratios and the Probabilistic Prediction of Bankruptcy (1980)Mining corporate annual reports for intelligent detection of financial statement fraud – A comparative study of machine learning methods (2017)Systematic review of bankruptcy prediction models: Towards a framework for tool selection (2017)Deep learning for financial applications : A survey (2020)Fuzzy sets (1965)Measuring the efficiency of decision making units (1978)Fuzzy sets (1996)Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey (2009)Using artificial neural network models in stock market index prediction (2011)Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman's Z‐ Score Model (2016)Statistical Classification Methods in Consumer Credit Scoring: A Review (1997)Forecasting bank failures and stress testing: A machine learning approach (2018)Integration of unsupervised and supervised machine learning algorithms for credit risk assessment (2019)Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending (2020)Corporate Ownership Around the World (1999)Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection (2020)CatBoost model and artificial intelligence techniques for corporate failure prediction (2021)Financial applications of machine learning: A literature review (2023)Forecasting with artificial neural networks: (1998)The effect of feature selection on financial distress prediction (2014)A hybrid ETS–ANN model for time series forecasting (2017)Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research (2015)A novel heterogeneous ensemble credit scoring model based on bstacking approach (2017)What's In A Name? Malay Seals As Onomastic Sources (2018)Machine learning-driven credit risk: a systemic review (2022)Operational Research and Artificial Intelligence Methods in Banking (2022)The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients (2008)An explainable artificial intelligence approach for financial distress prediction (2022)Machine Learning for Credit Risk Prediction: A Systematic Literature Review (2023)Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE (2020)Multi-class financial distress prediction based on support vector machines integrated with the decomposition and fusion methods (2021)
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References

  • 2006Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review1,068 cited
  • 2016Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study399 cited
  • 2013Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches394 cited
  • 2011Machine Learning in Financial Crisis Prediction: A Survey325 cited
  • 2016A comparative study on base classifiers in ensemble methods for credit scoring248 cited
  • 2015Financial credit risk assessment: a recent review234 cited
  • 2013Performance of corporate bankruptcy prediction models on imbalanced dataset: The effect of sampling methods215 cited
  • 2018An investigation of bankruptcy prediction in imbalanced datasets185 cited
  • 2011Combining cluster analysis with classifier ensembles to predict financial distress173 cited
  • 2018Exploring the synergetic effects of sample types on the performance of ensembles for credit risk and corporate bankruptcy prediction138 cited
  • 2019A CEEMDAN and XGBOOST‐Based Approach to Forecast Crude Oil Prices131 cited
  • 2017Integrated framework for profit-based feature selection and SVM classification in credit scoring111 cited
  • 2019Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets109 cited

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2019
Improving financial bankruptcy prediction in a highly imbalanced class distribution using oversampling and ensemble learning: a case from the Spanish market
107 cited
  • 2011Forecasting business failure: The use of nearest-neighbour support vectors and correcting imbalanced samples – Evidence from the Chinese hotel industry99 cited
  • 2019A Hybrid Approach Using Oversampling Technique and Cost‐Sensitive Learning for Bankruptcy Prediction92 cited
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