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Rough set-based feature selection for credit risk prediction using weight-adjusted boosting ensemble method
16 references·0 citing·26 second hop
Rough set-based feature selection for credit risk prediction using weight-adjusted boosting ensemble method (2019)Rough set-based feature selection for cre…Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research (2015)Benchmarking state-of-the-art…The effect of feature selection on financial distress prediction (2014)The effect of feature selecti…Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches (2013)Predicting financial distress…Machine Learning in Financial Crisis Prediction: A Survey (2011)Machine Learning in Financial…A comparative assessment of ensemble learning for credit scoring (2010)A comparative assessment of e…Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks (2008)Bankruptcy forecasting: An em…An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring (2008)An experimental comparison of…Using neural network ensembles for bankruptcy prediction and credit scoring (2007)Using neural network ensemble…Credit risk assessment with a multistage neural network ensemble learning approach (2007)Credit risk assessment with a…Credit scoring with a data mining approach based on support vector machines (2006)Credit scoring with a data mi…Benchmarking state-of-the-art classification algorithms for credit scoring (2003)Benchmarking state-of-the-art…Neural network credit scoring models (2000)Neural network credit scoring…A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers (2000)A survey of credit and behavi…A comparison of neural networks and linear scoring models in the credit union environment (1996)A comparison of neural networ…Managerial Applications of Neural Networks: The Case of Bank Failure Predictions (1992)Managerial Applications of Ne…Multivariate Normality and Forecasting of Business Bankruptcy (1987)Multivariate Normality and Fo…The Pricing of Options and Corporate Liabilities (1973)Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience) (1994)Recent developments in consumer credit risk assessment (2007)Statistical Classification Methods in Consumer Credit Scoring: A Review (1997)Credit rating analysis with support vector machines and neural networks: a market comparative study (2003)Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities (2015)Statistical and machine learning models in credit scoring: A systematic literature survey (2020)Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis (2021)A Stochastic Approximation Method (1951)CREDIT SCORING, STATISTICAL TECHNIQUES AND EVALUATION CRITERIA: A REVIEW OF THE LITERATURE (2011)Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects (2021)FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY (1968)Forecasting with artificial neural networks: (1998)Application of data mining techniques in customer relationship management: A literature review and classification (2008)Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters (2005)A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010)Financial Ratios As Predictors of Failure (1966)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)The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients (2008)A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring (2017)Machine behaviour (2019)'It's Reducing a Human Being to a Percentage' (2018)Machine Learning and Natural Language Processing in Mental Health: Systematic Review (2020)
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References

  • 2015Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research1,277 cited
  • 1992Managerial Applications of Neural Networks: The Case of Bank Failure Predictions1,121 cited
  • 2000Neural network credit scoring models1,032 cited
  • 2003Benchmarking state-of-the-art classification algorithms for credit scoring903 cited
  • 2006Credit scoring with a data mining approach based on support vector machines805 cited
  • 2000A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers744 cited
  • 1996A comparison of neural networks and linear scoring models in the credit union environment522 cited
  • 2007Using neural network ensembles for bankruptcy prediction and credit scoring514 cited
  • 2010A comparative assessment of ensemble learning for credit scoring501 cited
  • 2013Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches394 cited
  • 1987Multivariate Normality and Forecasting of Business Bankruptcy354 cited
  • 2011Machine Learning in Financial Crisis Prediction: A Survey325 cited
  • 2007Credit risk assessment with a multistage neural network ensemble learning approach310 cited
  • 2008An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring

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290 cited
  • 2008Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks290 cited
  • 2014The effect of feature selection on financial distress prediction234 cited
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