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Ensemble classification based on supervised clustering for credit scoring
11 references·16 citing·39 second hop
Ensemble classification based on supervised clustering for credit scoring (2016)Ensemble classification based on supervis…Credit scoring using the clustered support vector machine (2014)Credit scoring using the clus…A comparative study of classifier ensembles for bankruptcy prediction (2014)A comparative study of classi…Consumer credit risk: Individual probability estimates using machine learning (2013)Consumer credit risk: Individ…Two credit scoring models based on dual strategy ensemble trees (2011)Two credit scoring models bas…Subagging for credit scoring models (2009)Subagging for credit scoring …Predicting corporate financial distress based on integration of support vector machine and logistic regression (2006)Predicting corporate financia…Building credit scoring models using genetic programming (2005)Building credit scoring model…A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines (2005)A two-stage hybrid credit sco…Credit rating analysis with support vector machines and neural networks: a market comparative study (2003)Credit rating analysis with s…Statistical Classification Methods in Consumer Credit Scoring: A Review (1997)Statistical Classification Me…A k-Nearest-Neighbour Classifier for Assessing Consumer Credit Risk (1996)A k-Nearest-Neighbour Classif…Detecting financial statement fraud using dynamic ensemble machine learning (2023)Detecting financial statement…Consumer credit risk assessment: A review from the state-of-the-art classification algorithms, data traits, and learning methods (2023)Consumer credit risk assessme…Credit scoring methods: Latest trends and points to consider (2022)Credit scoring methods: Lates…Bagging Supervised Autoencoder Classifier for credit scoring (2022)Bagging Supervised Autoencode…The implication of machine learning for financial solvency prediction: an empirical analysis on public listed companies of Bangladesh (2021)The implication of machine le…Multi-classification assessment of bank personal credit risk based on multi-source information fusion (2021)Multi-classification assessme…Credit scoring based on tree-enhanced gradient boosting decision trees (2021)Credit scoring based on tree-…Step-wise multi-grained augmented gradient boosting decision trees for credit scoring (2020)Step-wise multi-grained augme…Multi-view ensemble learning based on distance-to-model and adaptive clustering for imbalanced credit risk assessment in P2P lending (2020)Multi-view ensemble learning …A novel tree-based dynamic heterogeneous ensemble method for credit scoring (2020)A novel tree-based dynamic he…A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees (2020)A comparative study of foreca…A Comparative Performance Assessment of Ensemble Learning for Credit Scoring (2020)A Comparative Performance Ass…Machine learning models and cost-sensitive decision trees for bond rating prediction (2019)Machine learning models and c…Dynamic ensemble classification for credit scoring using soft probability (2018)Dynamic ensemble classificati…Classifier selection and clustering with fuzzy assignment in ensemble model for credit scoring (2018)Classifier selection and clus…A new hybrid ensemble credit scoring model based on classifiers consensus system approach (2016)A new hybrid ensemble credit …FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY (1968)A Discriminant Analysis of Predictors of Business Failure (1972)Forecasting with artificial neural networks: (1998)A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010)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)Applied Statistical Decision Theory (1962)Forecasting stock market crisis events using deep and statistical machine learning techniques (2018)Machine learning techniques for credit risk evaluation: a systematic literature review (2020)Financial Ratios As Predictors of Failure (1966)Financial Ratios and the Probabilistic Prediction of Bankruptcy (1980)Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey (2009)Combining cluster analysis with classifier ensembles to predict financial distress (2011)Financial distress prediction using support vector machines: Ensemble vs. individual (2012)Neural network credit scoring models (2000)Using neural network ensembles for bankruptcy prediction and credit scoring (2007)Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks (2014)Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring (2019)Combining corporate governance indicators with stacking ensembles for financial distress prediction (2020)Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress (1985)Benchmarking state-of-the-art classification algorithms for credit scoring (2003)A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring (2015)A novel heterogeneous ensemble credit scoring model based on bstacking approach (2017)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)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)A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior (1980)PREDICTING BANKRUPTCY FOR FIRMS IN FINANCIAL DISTRESS (1990)Credit scoring and rejected instances reassigning through evolutionary computation techniques (2003)Predicting and Deterring Default with Social Media Information in Peer-to-Peer Lending (2017)Investigation and improvement of multi-layer perceptron neural networks for credit scoring (2014)The effect of feature selection on financial distress prediction (2014)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)
this work references cited by second hop

References

  • 1997Statistical Classification Methods in Consumer Credit Scoring: A Review1,039 cited
  • 2003Credit rating analysis with support vector machines and neural networks: a market comparative study1,017 cited
  • 2005A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines389 cited
  • 2005Building credit scoring models using genetic programming356 cited
  • 1996A k-Nearest-Neighbour Classifier for Assessing Consumer Credit Risk286 cited
  • 2014Credit scoring using the clustered support vector machine269 cited
  • 2011Two credit scoring models based on dual strategy ensemble trees244 cited
  • 2006Predicting corporate financial distress based on integration of support vector machine and logistic regression229 cited
  • 2009Subagging for credit scoring models213 cited
  • 2014A comparative study of classifier ensembles for bankruptcy prediction201 cited
  • 2013Consumer credit risk: Individual probability estimates using machine learning196 cited

Cited by

  • 2016A new hybrid ensemble credit scoring model based on classifiers consensus system approach161 cited
  • 2020A Comparative Performance Assessment of Ensemble Learning for Credit Scoring152 cited
  • 2021Credit scoring based on tree-enhanced gradient boosting decision trees

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133 cited
  • 2020A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees123 cited
  • 2018Dynamic ensemble classification for credit scoring using soft probability118 cited
  • 2020A novel tree-based dynamic heterogeneous ensemble method for credit scoring109 cited
  • 2020Multi-view ensemble learning based on distance-to-model and adaptive clustering for imbalanced credit risk assessment in P2P lending102 cited
  • 2020Step-wise multi-grained augmented gradient boosting decision trees for credit scoring81 cited
  • 2022Credit scoring methods: Latest trends and points to consider73 cited
  • 2023Consumer credit risk assessment: A review from the state-of-the-art classification algorithms, data traits, and learning methods71 cited
  • 2023Detecting financial statement fraud using dynamic ensemble machine learning70 cited
  • 2019Machine learning models and cost-sensitive decision trees for bond rating prediction66 cited
  • 2022Bagging Supervised Autoencoder Classifier for credit scoring66 cited
  • 2021Multi-classification assessment of bank personal credit risk based on multi-source information fusion59 cited
  • 2021The implication of machine learning for financial solvency prediction: an empirical analysis on public listed companies of Bangladesh55 cited
  • 2018Classifier selection and clustering with fuzzy assignment in ensemble model for credit scoring55 cited