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A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring
11 references·16 citing·41 second hop
A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring (2015)A hybrid data mining model of feature sel…The effect of feature selection on financial distress prediction (2014)The effect of feature selecti…Genetic algorithm-based heuristic for feature selection in credit risk assessment (2013)Genetic algorithm-based heuri…A comparative assessment of ensemble learning for credit scoring (2010)A comparative assessment of e…Subagging for credit scoring models (2009)Subagging for credit scoring …Combination of feature selection approaches with SVM in credit scoring (2009)Combination of feature select…The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients (2008)The comparisons of data minin…Feature selection in bankruptcy prediction (2008)Feature selection in bankrupt…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…Building credit scoring models using genetic programming (2005)Building credit scoring model…A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers (2000)A survey of credit and behavi…Consumer credit risk assessment: A review from the state-of-the-art classification algorithms, data traits, and learning methods (2023)Consumer credit risk assessme…Integrating data augmentation and hybrid feature selection for small sample credit risk assessment with high dimensionality (2022)Integrating data augmentation…A PCA-AdaBoost model for E-commerce customer churn prediction (2022)A PCA-AdaBoost model for E-co…The application of PROMETHEE multi-criteria decision aid in financial decision making: Case of distress prediction models evaluation (2020)The application of PROMETHEE …Step-wise multi-grained augmented gradient boosting decision trees for credit scoring (2020)Step-wise multi-grained augme…New hybrid data mining model for credit scoring based on feature selection algorithm and ensemble classifiers (2020)New hybrid data mining model …Mobile payment and online to offline retail business models (2020)Mobile payment and online to …A study on credit scoring modeling with different feature selection and machine learning approaches (2020)A study on credit scoring mod…Machine learning models and cost-sensitive decision trees for bond rating prediction (2019)Machine learning models and c…Automatic detection of relationships between banking operations using machine learning (2019)Automatic detection of relati…An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments (2019)An Empirical Comparison of Ma…A XGBOOST RISK MODEL VIA FEATURE SELECTION AND BAYESIAN HYPER-PARAMETER OPTIMIZATION (2019)A XGBOOST RISK MODEL VIA FEAT…A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks (2019)A dynamic credit risk assessm…Exploring the synergetic effects of sample types on the performance of ensembles for credit risk and corporate bankruptcy prediction (2018)Exploring the synergetic effe…Digitalisation and Big Data Mining in Banking (2018)Digitalisation and Big Data M…Using an ensemble classifier based on sequential floating forward selection for financial distress prediction problem (2016)Using an ensemble classifier …Financial Ratios As Predictors of Failure (1966)FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY (1968)Managerial Applications of Neural Networks: The Case of Bank Failure Predictions (1992)Two credit scoring models based on dual strategy ensemble trees (2011)Financial credit risk assessment: a recent review (2015)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)Neural network credit scoring models (2000)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)An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data (2012)A comparative study on base classifiers in ensemble methods for credit scoring (2016)What's In A Name? Malay Seals As Onomastic Sources (2018)Credit scoring with a data mining approach based on support vector machines (2006)Consumer credit-risk models via machine-learning algorithms (2010)Research design and statistical analysis (2011)Information gain directed genetic algorithm wrapper feature selection for credit rating (2018)COVID-19 and stock market volatility: An industry level analysis (2020)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)CREDIT SCORING, STATISTICAL TECHNIQUES AND EVALUATION CRITERIA: A REVIEW OF THE LITERATURE (2011)Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress (1985)Benchmarking state-of-the-art classification algorithms for credit scoring (2003)A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach (2011)A comparative study of classifier ensembles for bankruptcy prediction (2014)A novel heterogeneous ensemble credit scoring model based on bstacking approach (2017)35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems (2005)Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review (2006)Classifiers consensus system approach for credit scoring (2016)Combining corporate governance indicators with stacking ensembles for financial distress prediction (2020)Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering (2022)A comparison of neural networks and linear scoring models in the credit union environment (1996)Business failure prediction using rough sets (1999)Forecasting energy consumption using a grey model improved by incorporating genetic programming (2010)Machine Learning in Financial Crisis Prediction: A Survey (2011)Machine Learning in Banking Risk Management: A Literature Review (2019)A Review on Fairness in Machine Learning (2022)
this work references cited by second hop

References

  • 2008The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients881 cited
  • 2000A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers744 cited
  • 2007Using neural network ensembles for bankruptcy prediction and credit scoring514 cited
  • 2010A comparative assessment of ensemble learning for credit scoring501 cited
  • 2013Genetic algorithm-based heuristic for feature selection in credit risk assessment430 cited
  • 2005Building credit scoring models using genetic programming356 cited
  • 2008An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring290 cited
  • 2008Feature selection in bankruptcy prediction246 cited
  • 2014The effect of feature selection on financial distress prediction234 cited
  • 2009Subagging for credit scoring models213 cited
  • 2009Combination of feature selection approaches with SVM in credit scoring202 cited

Cited by

  • 2019An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments143 cited
  • 2019A XGBOOST RISK MODEL VIA FEATURE SELECTION AND BAYESIAN HYPER-PARAMETER OPTIMIZATION142 cited
  • 2018Exploring the synergetic effects of sample types on the performance of ensembles for credit risk and corporate bankruptcy prediction

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138 cited
  • 2020A study on credit scoring modeling with different feature selection and machine learning approaches135 cited
  • 2018Digitalisation and Big Data Mining in Banking132 cited
  • 2020Mobile payment and online to offline retail business models108 cited
  • 2019A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks106 cited
  • 2020New hybrid data mining model for credit scoring based on feature selection algorithm and ensemble classifiers83 cited
  • 2020The application of PROMETHEE multi-criteria decision aid in financial decision making: Case of distress prediction models evaluation82 cited
  • 2020Step-wise multi-grained augmented gradient boosting decision trees for credit scoring81 cited
  • 2016Using an ensemble classifier based on sequential floating forward selection for financial distress prediction problem77 cited
  • 2023Consumer credit risk assessment: A review from the state-of-the-art classification algorithms, data traits, and learning methods71 cited
  • 2019Machine learning models and cost-sensitive decision trees for bond rating prediction66 cited
  • 2022Integrating data augmentation and hybrid feature selection for small sample credit risk assessment with high dimensionality59 cited
  • 2019Automatic detection of relationships between banking operations using machine learning58 cited
  • 2022A PCA-AdaBoost model for E-commerce customer churn prediction54 cited