Communications of the ACM
Communications of the ACM - Special issue on parallelism
Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
A universal theorem on learning curves
Neural Networks
Fuzzy logic, neural networks, and soft computing
Communications of the ACM
An introduction to computational learning theory
An introduction to computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Data mining with neural networks: solving business problems from application development to decision support
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
A statistical perspective on knowledge discovery in databases
Advances in knowledge discovery and data mining
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Building, using, and managing the data warehouse
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Machine Learning
Proceedings of the Second International Conference on Knowledge Discovery and Data Mining
Proceedings of the Second International Conference on Knowledge Discovery and Data Mining
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Statistical Themes and Lessons for Data Mining
Data Mining and Knowledge Discovery
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
A neural network classification of credit applicants in consumer credit scoring
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
e-banking prediction using data mining methods
AIKED'05 Proceedings of the 4th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering Data Bases
Modelling municipal rating by cluster analysis and neural networks
NN'06 Proceedings of the 7th WSEAS International Conference on Neural Networks
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
Choosing the best set of variables in regression analysis using integer programming
Journal of Global Optimization
An Analysis of Support Vector Machines for Credit Risk Modeling
Proceedings of the 2008 conference on Applications of Data Mining in E-Business and Finance
A Neural Approach for SME's Credit Risk Analysis in Turkey
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Multi-step methods for choosing the best set of variables in regression analysis
Computational Optimization and Applications
Municipal credit rating modelling by neural networks
Decision Support Systems
Knowledge discovery using neural approach for SME's credit risk analysis problem in Turkey
Expert Systems with Applications: An International Journal
Decision tree-based technology credit scoring for start-up firms: Korean case
Expert Systems with Applications: An International Journal
A hybrid SOM-Altman model for bankruptcy prediction
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Relevance vector machine based infinite decision agent ensemble learning for credit risk analysis
Expert Systems with Applications: An International Journal
A logical analysis of banks' financial strength ratings
Expert Systems with Applications: An International Journal
Fuzzy classification method in credit risk
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Classification of companies using maximal margin ellipsoidal surfaces
Computational Optimization and Applications
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Risk assessment of financialintermediaries is an area of renewed interest due tothe financial crises of the 1980's and 90's. Anaccurate estimation of risk, and its use in corporateor global financial risk models, could be translatedinto a more efficient use of resources. One importantingredient to accomplish this goal is to find accuratepredictors of individual risk in the credit portfoliosof institutions. In this context we make a comparativeanalysis of different statistical and machine learningmodeling methods of classification on a mortgage loandata set with the motivation to understand theirlimitations and potential. We introduced a specificmodeling methodology based on the study of errorcurves. Using state-of-the-art modeling techniques webuilt more than 9,000 models as part of the study. Theresults show that CART decision-tree models providethe best estimation for default with an average 8.31%error rate for a training sample of 2,000 records. Asa result of the error curve analysis for this model weconclude that if more data were available,approximately 22,000 records, a potential 7.32% errorrate could be achieved. Neural Networks provided thesecond best results with an average error of 11.00%.The K-Nearest Neighbor algorithm had an averageerror rate of 14.95%. These results outperformed thestandard Probit algorithm which attained an averageerror rate of 15.13%. Finally we discuss thepossibilities to use this type of accurate predictivemodel as ingredients of institutional and global riskmodels.