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Assessment of Support Vector Machine performance for default prediction and credit rating
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Assessment of Support Vector Machine performance for default prediction and credit rating (2022)Assessment of Support Vector Machine perf…Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring (2019)Application of new deep genet…The Advantages and Disadvantages of Using Qualitative and Quantitative Approaches and Methods in Language “Testing and Assessment” Research: A Literature Review (2016)The Advantages and Disadvanta…A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance (2015)A credit risk assessment mode…Selection of Support Vector Machines based classifiers for credit risk domain (2014)Selection of Support Vector M…Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes (2010)Neural networks for credit ri…Support vector machines for credit scoring and discovery of significant features (2008)Support vector machines for c…Credit scoring with a data mining approach based on support vector machines (2006)Credit scoring with a data mi…Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review (2006)Bankruptcy prediction in bank…Benchmarking state-of-the-art classification algorithms for credit scoring (2003)Benchmarking state-of-the-art…Credit scoring using the hybrid neural discriminant technique (2002)Credit scoring using the hybr…Neural network credit scoring models (2000)Neural network credit scoring…A survey of business failures with an emphasis on prediction methods and industrial applications (1996)A survey of business failures…Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience) (1994)Corporate distress diagnosis:…Recognizing Financial Distress Patterns Using a Neural Network Tool (1993)Recognizing Financial Distres…Financial Ratios and the Probabilistic Prediction of Bankruptcy (1980)Financial Ratios and the Prob…ZETATM analysis A new model to identify bankruptcy risk of corporations (1977)ZETATM analysis A new model t…FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY (1968)Forecasting Bankruptcy More Accurately: A Simple Hazard Model (2001)Financial Ratios As Predictors of Failure (1966)Organizational failure: a critique of recent research and a proposed integrative framework (2004)Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey (2009)ELECTRE: A comprehensive literature review on methodologies and applications (2015)Conditional logit analysis of qualitative choice behavior (1972)The Effect of Audit Quality on Earnings Management* (1998)Methodological Issues Related to the Estimation of Financial Distress Prediction Models (1984)Credit risk measurement: Developments over the last 20 years (1997)Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks (1998)A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010)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)Fuzzy sets (1965)Measuring the efficiency of decision making units (1978)Fuzzy sets (1996)Using artificial neural network models in stock market index prediction (2011)Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman's Z‐ Score Model (2016)Credit rating analysis with support vector machines and neural networks: a market comparative study (2003)Machine behaviour (2019)Basics of Qualitative Research (1992)Handbook of Qualitative Research (1994)Research Methods in Education (2007)QUALITATIVE RESEARCH METHODOLOGY IN SOCIAL SCIENCES AND RELATED SUBJECTS (2018)Usability factors predicting continuance of intention to use cloud e-learning application (2019)Application of triangulation in qualitative research (2023)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)The effects of government ownership on bank lending (2003)
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References

  • 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy6,005 cited
  • 1977ZETATM analysis A new model to identify bankruptcy risk of corporations1,812 cited
  • 2016The Advantages and Disadvantages of Using Qualitative and Quantitative Approaches and Methods in Language “Testing and Assessment” Research: A Literature Review1,278 cited
  • 2006Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review1,068 cited
  • 1994Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)1,059 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
  • 1996A survey of business failures with an emphasis on prediction methods and industrial applications591 cited
  • 1993Recognizing Financial Distress Patterns Using a Neural Network Tool470 cited
  • 2002Credit scoring using the hybrid neural discriminant technique355 cited
  • 2008Support vector machines for credit scoring and discovery of significant features336 cited
  • 2010Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes236 cited
  • 2019

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Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring
176 cited
  • 2014Selection of Support Vector Machines based classifiers for credit risk domain142 cited
  • 2015A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance112 cited
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