Bio-Inspired Credit Risk Analysis: Computational Intelligence with Support Vector MachinesSpringer Science & Business Media, 2008. gada 24. apr. - 244 lappuses Credit risk analysis is one of the most important topics in the field of financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. Especially for some credit-granting institutions such as commercial banks and credit companies, the ability to discriminate good customers from bad ones is crucial. The need for reliable quantitative models that predict defaults accurately is imperative so that the interested parties can take either preventive or corrective action. Hence credit risk analysis becomes very important for sustainability and profit of enterprises. In such backgrounds, this book tries to integrate recent emerging support vector machines and other computational intelligence techniques that replicate the principles of bio-inspired information processing to create some innovative methodologies for credit risk analysis and to provide decision support information for interested parties. |
Saturs
Credit Risk Assessment Using a NearestPointAlgorithmbased | 27 |
Credit Risk Evaluation Using SVM with Direct Search for Parame | 40 |
Part III | 56 |
5A Least Squares Fuzzy SVM Approach to Credit Risk Assessment | 73 |
Evaluating Credit Risk with a BilateralWeighted Fuzzy | 85 |
Credit Risk Analysis with a SVMbased Metamodeling Ensemble | 157 |
An EvolutionaryProgrammingBased Knowledge Ensemble Model | 178 |
An IntelligentAgentBased Multicriteria Fuzzy Group Decision | 197 |
Biographies of Four Authors of the Book | 243 |
Citi izdevumi - Skatīt visu
Bio-Inspired Credit Risk Analysis: Computational Intelligence with Support ... Lean Yu,Shouyang Wang,Kin Keung Lai,Ligang Zhou Priekšskatījums nav pieejams - 2010 |
Bio-Inspired Credit Risk Analysis: Computational Intelligence with Support ... Lean Yu,Shouyang Wang,Kin Keung Lai,Ligang Zhou Priekšskatījums nav pieejams - 2008 |
Bieži izmantoti vārdi un frāzes
accuracy algorithm analysis applicants approach attributes average base models better BPNN chapter classification combination comparison computational construct credit dataset credit risk evaluation credit scoring criteria dataset decision determined discriminant ensemble model error et al example experiments final firms fitness function fuzzy GDM fuzzy SVM GDM model grid hybrid important improve increase individual input intelligent kernel function knowledge learning linear LSSVM membership metalearning metamodel metamodeling technique methods mining multicriteria neural network nonlinear Note objective obtained optimal original output parameters performance prediction presented problem programming proposed reason reduce rough set rule sample selection shown similar single solution solving space stage standard step strategy subset support vector machines SVM model SVM-based Table techniques tion training data Type variables weighted
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