Bio-Inspired Credit Risk Analysis

Cover -- Table of Contents -- Part I: Credit Risk Analysis with Computational Intelligence -- Credit Risk Analysis with Computational Intelligence: A Review -- 1.1 Introduction -- 1.2 Literature Collection -- 1.3 Literature Investigation and Analysis -- 1.4 Implications on Valuable Research Topics -- 1.5 Conclusions -- Part II: Unitary SVM Models with Optimal Parameter Selection for Credit Risk Evaluation -- Credit Risk Assessment Using a Nearest-Point-Algorithm-based SVM with Design of Experiment for Parameter Selection -- 2.1 Introduction -- 2.2 SVM with Nearest Point Algorithm -- 2.3 DOE-based Parameter Selection for SVM with NPA -- 2.4 Experimental Analysis -- 2.5 Conclusions -- Credit Risk Evaluation Using SVM with Direct Search for Parameter Selection -- 3.1 Introduction -- 3.2 Methodology Description -- 3.3 Experimental Study -- 3.4 Conclusions -- Part III: Hybridizing SVM and Other Computational Intelligent Techniques for Credit Risk Analysis -- Hybridizing Rough Sets and SVM for Credit Risk Evaluation -- 4.1 Introduction -- 4.2 Preliminaries of Rough Sets and SVM -- 4.3 Proposed Hybrid Intelligent Mining System -- 4.4 Experiment Study -- 4.5 Concluding Remarks -- A Least Squares Fuzzy SVM Approach to Credit Risk Assessment -- 5.1 Introduction -- 5.2 Least Squares Fuzzy SVM -- 5.3 Experiment Analysis -- 5.4 Conclusions -- Evaluating Credit Risk with a Bilateral-Weighted Fuzzy SVM Model -- 6.1 Introduction -- 6.2 Formulation of the Bilateral-Weighted Fuzzy SVM Model -- 6.3 Empirical Analysis -- 6.4 Conclusions -- Evolving Least Squares SVM for Credit Risk Analysis -- 7.1 Introduction -- 7.2 SVM and LSSVM -- 7.3 Evolving LSSVM Learning Paradigm -- 7.4 Research Data and Comparable Models -- 7.5 Experimental Results -- 7.6 Conclusions -- Part IV: SVM Ensemble Learning for Credit Risk Analysis -- Credit Risk Evaluation Using a Multistage SVM Ensemble Learning Approach -- 8.1 Introduction -- 8.2 Previous Studies -- 8.3 Formulation of SVM Ensemble Learning Paradigm -- 8.4 Empirical Analysis -- 8.5 Conclusions -- Credit Risk Analysis with a SVM-based Metamodeling Ensemble Approach -- 9.1 Introduction -- 9.2 SVM-based Metamodeling Process -- 9.3 Experimental Analyses -- 9.4 Conclusions -- An Evolutionary-Programming-Based Knowledge Ensemble Model for Business Credit Risk Analysis -- 10.1 Introduction -- 10.2 EP-Based Knowledge Ensemble Methodology -- 10.3 Research Data and Experiment Design -- 10.4 Experiment Results -- 10.5 Conclusions -- An Intelligent-Agent-Based Multicriteria Fuzzy Group Decision Making Model for Credit Risk Analysis -- 11.1 Introduction -- 11.2 Methodology Formulation -- 11.3 Experimental Study -- 11.4 Conclusions and Future Directions -- References -- Subject Index -- Biographies of Four Authors of the Book.