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A novel hybrid ensemble model based on tree-based method and deep learning method for default prediction
16 references·7 citing·35 second hop
A novel hybrid ensemble model based on tree-based method and deep learning method for default prediction (2021)A novel hybrid ensemble model based on tr…Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning (2018)Study on a prediction of P2P …A novel heterogeneous ensemble credit scoring model based on bstacking approach (2017)A novel heterogeneous ensembl…The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending (2016)The use of profit scoring as …Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction (2016)Ensemble boosted trees with s…Classifiers consensus system approach for credit scoring (2016)Classifiers consensus system …A new hybrid ensemble credit scoring model based on classifiers consensus system approach (2016)A new hybrid ensemble credit …A comparative study on base classifiers in ensemble methods for credit scoring (2016)A comparative study on base c…Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research (2015)Benchmarking state-of-the-art…Exploring the behaviour of base classifiers in credit scoring ensembles (2012)Exploring the behaviour of ba…Two credit scoring models based on dual strategy ensemble trees (2011)Two credit scoring models bas…Combining cluster analysis with classifier ensembles to predict financial distress (2011)Combining cluster analysis wi…Credit rating analysis with support vector machines and neural networks: a market comparative study (2003)Credit rating analysis with s…Neural network credit scoring models (2000)Neural network credit scoring…Statistical Classification Methods in Consumer Credit Scoring: A Review (1997)Statistical Classification Me…ASSESSING THE VULNERABILITY TO FAILURE OF AMERICAN INDUSTRIAL FIRMS: A LOGISTIC ANALYSIS (1985)ASSESSING THE VULNERABILITY T…FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY (1968)FINANCIAL RATIOS, DISCRIMINAN…Machine learning techniques in bankruptcy prediction: A systematic literature review (2024)Machine learning techniques i…Advancing financial resilience: A systematic review of default prediction models and future directions in credit risk management (2024)Advancing financial resilienc…Improved credit risk prediction based on an integrated graph representation learning approach with graph transformation (2023)Improved credit risk predicti…Forecasting carbon price trends based on an interpretable light gradient boosting machine and Bayesian optimization (2023)Forecasting carbon price tren…Enhancing credit risk prediction based on ensemble tree‐based feature transformation and logistic regression (2023)Enhancing credit risk predict…What should lenders be more concerned about? Developing a profit-driven loan default prediction model (2022)What should lenders be more c…Multi-view representation learning with Kolmogorov-Smirnov to predict default based on imbalanced and complex dataset (2022)Multi-view representation lea…Financial Ratios and the Probabilistic Prediction of Bankruptcy (1980)Econometric Models and Economic Forecasts (1990)Business failure prediction using rough sets (1999)The chairman’s statement ‐ A content analysis of discretionary narrative disclosures (2000)Research Methods in Accounting (2003)The cost of capital, corporation finance and the theory of investment (2008)A Discriminant Analysis of Predictors of Business Failure (1972)Managerial Applications of Neural Networks: The Case of Bank Failure Predictions (1992)Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters (2005)Credit scoring with a data mining approach based on support vector machines (2006)Consumer credit-risk models via machine-learning algorithms (2010)Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience) (1994)Benchmarking state-of-the-art classification algorithms for credit scoring (2003)Financial Ratios As Predictors of Failure (1966)Predicting failure in the U.S. banking sector: An extreme gradient boosting approach (2018)CatBoost model and artificial intelligence techniques for corporate failure prediction (2021)Efficient Capital Markets: A Review of Theory and Empirical Work (1970)EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* (1970)The Market for "Lemons": Quality Uncertainty and the Market Mechanism (1970)Cost-sensitive boosted tree for loan evaluation in peer-to-peer lending (2017)Soft consensus cost models for group decision making and economic interpretations (2019)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)Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending (2014)Home Bias in Online Investments: An Empirical Study of an Online Crowdfunding Market (2015)A study on predicting loan default based on the random forest algorithm (2019)Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series (2019)LightGBM: an Effective Decision Tree Gradient Boosting Method to Predict Customer Loyalty in the Finance Industry (2019)Managerial incentives and risk-taking☆ (2005)How does financial reporting quality relate to investment efficiency? (2009)A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring (2017)'It's Reducing a Human Being to a Percentage' (2018)Machine Learning and Natural Language Processing in Mental Health: Systematic Review (2020)The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients (2008)
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References

  • 1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY14,869 cited
  • 2015Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research1,277 cited
  • 1997Statistical Classification Methods in Consumer Credit Scoring: A Review1,039 cited
  • 2000Neural network credit scoring models1,032 cited
  • 2003Credit rating analysis with support vector machines and neural networks: a market comparative study1,017 cited
  • 1985ASSESSING THE VULNERABILITY TO FAILURE OF AMERICAN INDUSTRIAL FIRMS: A LOGISTIC ANALYSIS559 cited
  • 2018Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning444 cited
  • 2016Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction419 cited
  • 2016The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending253 cited
  • 2016A comparative study on base classifiers in ensemble methods for credit scoring248 cited
  • 2011Two credit scoring models based on dual strategy ensemble trees244 cited
  • 2017A novel heterogeneous ensemble credit scoring model based on bstacking approach214 cited
  • 2016Classifiers consensus system approach for credit scoring202 cited
  • 2011

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Combining cluster analysis with classifier ensembles to predict financial distress
173 cited
  • 2016A new hybrid ensemble credit scoring model based on classifiers consensus system approach161 cited
  • 2012Exploring the behaviour of base classifiers in credit scoring ensembles142 cited
  • Cited by

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    • 2024Machine learning techniques in bankruptcy prediction: A systematic literature review62 cited
    • 2023Improved credit risk prediction based on an integrated graph representation learning approach with graph transformation53 cited
    • 2022What should lenders be more concerned about? Developing a profit-driven loan default prediction model38 cited
    • 2024Advancing financial resilience: A systematic review of default prediction models and future directions in credit risk management38 cited
    • 2022Multi-view representation learning with Kolmogorov-Smirnov to predict default based on imbalanced and complex dataset25 cited
    • 2023Enhancing credit risk prediction based on ensemble tree‐based feature transformation and logistic regression16 cited