SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
A Hybrid Credit Scoring Model Based on Genetic Programming and Support Vector Machines
12 references·2 citing·35 second hop
A Hybrid Credit Scoring Model Based on Genetic Programming and Support Vector Machines (2008)A Hybrid Credit Scoring Model Based on Ge…Credit Scoring and Its Applications (2017)Credit Scoring and Its Applic…A Comparison Study of Credit Scoring Models (2007)A Comparison Study of Credit …Neural Network Metalearning for Credit Scoring (2006)Neural Network Metalearning f…Hybrid mining approach in the design of credit scoring models (2005)Hybrid mining approach in the…Building credit scoring models using genetic programming (2005)Building credit scoring model…Mining the customer credit using classification and regression tree and multivariate adaptive regression splines (2004)Mining the customer credit us…Credit rating analysis with support vector machines and neural networks: a market comparative study (2003)Credit rating analysis with s…Credit Scoring and Its Applications (2002)Credit Scoring and Its Applic…Neural network credit scoring models (2000)Neural network credit scoring…Statistical aspects of credit scoring (1995)Statistical aspects of credit…Machine-learning algorithms for credit-card applications (1992)Machine-learning algorithms f…Commercial Bank Lending: Process, Credit Scoring, and Costs of Errors in Lending (1980)Commercial Bank Lending: Proc…A data mining application in credit scoring processes of small and medium enterprises commercial corporate customers (2019)A data mining application in …Statistical and data mining methods in credit scoring (2016)Statistical and data mining m…Managerial Applications of Neural Networks: The Case of Bank Failure Predictions (1992)A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers (2000)Credit rating by hybrid machine learning techniques (2009)Combining cluster analysis with classifier ensembles to predict financial distress (2011)Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring (2019)A Discriminant Analysis of Predictors of Business Failure (1972)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, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY (1968)ZETATM analysis A new model to identify bankruptcy risk of corporations (1977)A credit scoring model for development banks: an African case study (1993)Stochastic Capacity Investment and Flexible vs. Dedicated Technology Choice in Imperfect Capital Markets (2011)Corporate distress prediction in China: a machine learning approach (2018)A comparison of neural networks and linear scoring models in the credit union environment (1996)Business failure prediction using rough sets (1999)A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010)Forecasting energy consumption using a grey model improved by incorporating genetic programming (2010)Machine Learning in Financial Crisis Prediction: A Survey (2011)Forecasting with artificial neural networks: (1998)Computer assisted customer churn management: State-of-the-art and future trends (2005)An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring (2008)Recent developments in consumer credit risk assessment (2007)A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees (2018)Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects (2021)An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring (2007)Credit scoring with macroeconomic variables using survival analysis (2008)Forecasting and operational research: a review (2008)Good practice in retail credit scorecard assessment (2005)A practical approach to credit scoring (2007)An artificial intelligence system for predicting customer default in e-commerce (2018)Credit risk assessment for unbalanced datasets based on data mining, artificial neural network and support vector machines (2018)Predicting credit risk on the basis of financial and non-financial variables and data mining (2018)
this work references cited by second hop

References

  • 2000Neural network credit scoring models1,032 cited
  • 2003Credit rating analysis with support vector machines and neural networks: a market comparative study1,017 cited
  • 2002Credit Scoring and Its Applications669 cited
  • 2004Mining the customer credit using classification and regression tree and multivariate adaptive regression splines378 cited
  • 2005Building credit scoring models using genetic programming356 cited
  • 2017Credit Scoring and Its Applications306 cited
  • 2005Hybrid mining approach in the design of credit scoring models196 cited
  • 1992Machine-learning algorithms for credit-card applications128 cited
  • 1995Statistical aspects of credit scoring97 cited
  • 1980Commercial Bank Lending: Process, Credit Scoring, and Costs of Errors in Lending92 cited
  • 2007A Comparison Study of Credit Scoring Models35 cited
  • 2006Neural Network Metalearning for Credit Scoring34 cited

Cited by

  • 2016Statistical and data mining methods in credit scoring25 cited
  • 2019A data mining application in credit scoring processes of small and medium enterprises commercial corporate customers23 cited

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work