| Credit Risk Analysis with Computational Intelligence: An Analytical Survey | p. 1 |
| Credit Risk Analysis with Computational Intelligence: A Review | p. 3 |
| Introduction | p. 3 |
| Literature Collection | p. 5 |
| Literature Investigation and Analysis | p. 7 |
| What is Credit Risk Evaluation Problem? | p. 8 |
| Typical Techniques for Credit Risk Analysis | p. 8 |
| Comparisons of Models | p. 17 |
| Implications on Valuable Research Topics | p. 23 |
| Conclusions | p. 24 |
| Unitary SVM Models with Optimal Parameter Selection for Credit Risk Evaluation | p. 25 |
| Credit Risk Assessment Using a Nearest-Point-Algorithm-basedSVM with Design of Experiment for Parameter Selection | p. 27 |
| Introduction | p. 27 |
| SVM with Nearest Point Algorithm | p. 29 |
| DOE-based Parameter Selection for SVM with NPA | p. 33 |
| Experimental Analysis | p. 35 |
| Conclusions | p. 38 |
| Credit Risk Evaluation Using SVM with Direct Search for Parameter Selection | p. 41 |
| Introduction | p. 41 |
| Methodology Description | p. 43 |
| Brief Review of LSSVM | p. 43 |
| Direct Search for Parameter Selection | p. 45 |
| Experimental Study | p. 47 |
| Research Data | p. 47 |
| Parameter Selection with Genetic Algorithm | p. 48 |
| Parameters Selection with Grid Search | p. 49 |
| Experimental Results | p. 50 |
| Conclusions | p. 54 |
| Hybridizing SVM and Other Computational Intelligent Techniquesfor Credit Risk Analysis | p. 57 |
| Hybridizing Rough Sets and SVM for Credit Risk Evaluation | p. 59 |
| Introduction | p. 59 |
| Preliminaries of Rough Sets and SVM | p. 61 |
| Basic Concepts of Rough Sets | p. 61 |
| Basic Ideas of Support Vector Machines | p. 62 |
| Proposed Hybrid Intelligent Mining System | p. 63 |
| General Framework of Hybrid Intelligent Mining System | p. 63 |
| 2D-Reductions by Rough Sets | p. 64 |
| Feature Selection by SVM | p. 65 |
| Rule Generation by Rough Sets | p. 66 |
| General Procedure of the Hybrid Intelligent Mining System | p. 67 |
| Experiment Study | p. 68 |
| Corporation Credit Dataset | p. 69 |
| Consumer Credit Dataset | p. 70 |
| Concluding Remarks | p. 72 |
| A Least Squares Fuzzy SVM Approach to Credit Risk Assessment | p. 73 |
| Introduction | p. 73 |
| Least Squares Fuzzy SVM | p. 74 |
| SVM | p. 74 |
| FSVM | p. 77 |
| Least Squares FSVM | p. 79 |
| Experiment Analysis | p. 81 |
| Conclusions | p. 84 |
| Evaluating Credit Risk with a Bilateral-Weighted Fuzzy SVMModel | p. 85 |
| Introduction | p. 85 |
| Formulation of the Bilateral-Weighted Fuzzy SVM Model | p. 89 |
| Bilateral-Weighting Errors | p. 89 |
| Formulation Process of the Bilateral-weighted fuzzy SVM | p. 91 |
| Generating Membership | p. 93 |
| Empirical Analysis | p. 95 |
| Dataset 1: UK Case | p. 96 |
| Dataset 2: Japanese Case | p. 98 |
| Dataset 3: England Case | p. 100 |
| Conclusions | p. 102 |
| Evolving Least Squares SVM for Credit Risk Analysis | p. 105 |
| Introduction | p. 105 |
| SVM and LSSVM | p. 108 |
| Evolving LSSVM Learning Paradigm | p. 111 |
| General Framework of Evolving LSSVM Learning Method | p. 111 |
| GA-based Input Features Evolution | p. 113 |
| GA-based Parameters Evolution | p. 117 |
| Research Data and Comparable Models | p. 119 |
| Research Data | p. 119 |
| Overview of Other Comparable Classification Models | p. 121 |
| Experimental Results | p. 123 |
| Empirical Analysis of GA-based Input Features Evolution | p. 123 |
| Empirical Analysis of GA-based Parameters Optimization | p. 126 |
| Comparisons with Other Classification Models | p. 129 |
| Conclusions | p. 131 |
| SVM Ensemble Learning for Credit Risk Analysis | p. 133 |
| Credit Risk Evaluation Using a Multistage SVM Ensemble Learning Approach | p. 135 |
| Introduction | p. 135 |
| Previous Studies | p. 138 |
| Formulation of SVM Ensemble Learning Paradigm | p. 140 |
| Partitioning Original Data Set | p. 140 |
| Creating Diverse Neural Network Classifiers | p. 142 |
| SVM Learning and Confidence Value Generation | p. 143 |
| Selecting Appropriate Ensemble Members | p. 144 |
| Reliability Value Transformation | p. 146 |
| Integrating Multiple Classifiers into an Ensemble Output | p. 146 |
| Empirical Analysis | p. 148 |
| Consumer Credit Risk Assessment | p. 149 |
| Corporation Credit Risk Assessment | p. 151 |
| Conclusions | p. 154 |
| Credit Risk Analysis with a SVM-based Metamodeling Ensemble Approach | p. 157 |
| Introduction | p. 157 |
| SVM-based Metamodeling Process | p. 160 |
| A Generic Metalearning Process | p. 160 |
| An Extended Metalearning Process | p. 163 |
| SVM-based Metamodeling Process | p. 165 |
| Experimental Analyses | p. 173 |
| Research Data and Experiment Design | p. 173 |
| Experimental Results | p. 174 |
| Conclusions | p. 177 |
| An Evolutionary-Programming-Based Knowledge Ensemble Modelfor Business Credit Risk Analysis | p. 179 |
| Introduction | p. 179 |
| EP-Based Knowledge Ensemble Methodology | p. 181 |
| Brief Introduction of Individual Data Mining Models | p. 182 |
| Knowledge Ensemble based on Individual Mining Results | p. 185 |
| Research Data and Experiment Design | p. 188 |
| Experiment Results | p. 189 |
| Results of Individual Models | p. 189 |
| Identification Performance of the Knowledge Ensemble | p. 191 |
| Identification Performance Comparisons | p. 193 |
| Conclusions | p. 195 |
| An Intelligent-Agent-Based Multicriteria Fuzzy Group DecisionMaking Model for Credit Risk Analysis | p. 197 |
| Introduction | p. 197 |
| Methodology Formulation | p. 201 |
| Experimental Study | p. 206 |
| An Illustrative Numerical Example | p. 206 |
| Empirical Comparisons with Different Credit Datasets | p. 208 |
| Conclusions and Future Directions | p. 221 |
| References | p. 223 |
| Subject Index | p. 239 |
| Biographies of Four Authors of the Book | p. 243 |
| Table of Contents provided by Publisher. All Rights Reserved. |
