Bio-Inspired Credit Risk Analysis

Transkript

1 Bio-Inspired Credit Risk Analysis Computational Intelligence with Support Vector Machines Bearbeitet von Lean Yu, Shouyang Wang, Kin Keung Lai, Ligang Zhou 1. Auflage Buch. XVI, 244 S. Hardcover ISBN Format (B x L): 15,5 x 23,5 cm Gewicht: 1210 g Weitere Fachgebiete > EDV, Informatik > Datenbanken, Informationssicherheit, Geschäftssoftware > Datenkompression, Dokumentaustauschformate Zu Leseprobe schnell und portofrei erhältlich bei Die Online-Fachbuchhandlung beck-shop.de ist spezialisiert auf Fachbücher, insbesondere Recht, Steuern und Wirtschaft. Im Sortiment finden Sie alle Medien (Bücher, Zeitschriften, CDs, ebooks, etc.) aller Verlage. Ergänzt wird das Programm durch Services wie Neuerscheinungsdienst oder Zusammenstellungen von Büchern zu Sonderpreisen. Der Shop führt mehr als 8 Millionen Produkte.

2 Part I Credit Risk Analysis with Computational Intelligence: An Analytical Survey Credit Risk Analysis with Computational Intelligence: A Review Introduction Literature Collection Literature Investigation and Analysis What is Credit Risk Evaluation Problem? Typical Techniques for Credit Risk Analysis Comparisons of Models Implications on Valuable Research Topics Conclusions...24 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 Introduction SVM with Nearest Point Algorithm DOE-based Parameter Selection for SVM with NPA Experimental Analysis Conclusions Credit Risk Evaluation Using SVM with Direct Search for Parameter Selection Introduction Methodology Description Brief Review of LSSVM Direct Search for Parameter Selection Experimental Study Research Data...47

3 XIV Parameter Selection with Genetic Algorithm Parameters Selection with Grid Search Experimental Results Conclusions...54 Part III Hybridizing SVM and Other Computational Intelligent Techniques for Credit Risk Analysis Hybridizing Rough Sets and SVM for Credit Risk Evaluation Introduction Preliminaries of Rough Sets and SVM Basic Concepts of Rough Sets Basic Ideas of Support Vector Machines Proposed Hybrid Intelligent Mining System General Framework of Hybrid Intelligent Mining System D-Reductions by Rough Sets Feature Selection by SVM Rule Generation by Rough Sets General Procedure of the Hybrid Intelligent Mining System Experiment Study Corporation Credit Dataset Consumer Credit Dataset Concluding Remarks A Least Squares Fuzzy SVM Approach to Credit Risk Assessment Introduction Least Squares Fuzzy SVM SVM FSVM Least Squares FSVM Experiment Analysis Conclusions Evaluating Credit Risk with a Bilateral-Weighted Fuzzy SVM Model Introduction Formulation of the Bilateral-Weighted Fuzzy SVM Model Bilateral-Weighting Errors Formulation Process of the Bilateral-weighted fuzzy SVM Generating Membership Empirical Analysis...95

4 XV Dataset 1: UK Case Dataset 2: Japanese Case Dataset 3: England Case Conclusions Evolving Least Squares SVM for Credit Risk Analysis Introduction SVM and LSSVM Evolving LSSVM Learning Paradigm General Framework of Evolving LSSVM Learning Method GA-based Input Features Evolution GA-based Parameters Evolution Research Data and Comparable Models Research Data Overview of Other Comparable Classification Models Experimental Results Empirical Analysis of GA-based Input Features Evolution Empirical Analysis of GA-based Parameters Optimization Comparisons with Other Classification Models Conclusions Part IV SVM Ensemble Learning for Credit Risk Analysis Credit Risk Evaluation Using a Multistage SVM Ensemble Learning Approach Introduction Previous Studies Formulation of SVM Ensemble Learning Paradigm Partitioning Original Data Set Creating Diverse Neural Network Classifiers SVM Learning and Confidence Value Generation Selecting Appropriate Ensemble Members Reliability Value Transformation Integrating Multiple Classifiers into an Ensemble Output Empirical Analysis Consumer Credit Risk Assessment Corporation Credit Risk Assessment Conclusions Credit Risk Analysis with a SVM-based Metamodeling Ensemble Approach...157

5 XVI 9.1 Introduction SVM-based Metamodeling Process A Generic Metalearning Process An Extended Metalearning Process SVM-based Metamodeling Process Experimental Analyses Research Data and Experiment Design Experimental Results Conclusions An Evolutionary-Programming-Based Knowledge Ensemble Model for Business Credit Risk Analysis Introduction EP-Based Knowledge Ensemble Methodology Brief Introduction of Individual Data Mining Models Knowledge Ensemble based on Individual Mining Results Research Data and Experiment Design Experiment Results Results of Individual Models Identification Performance of the Knowledge Ensemble Identification Performance Comparisons Conclusions An Intelligent-Agent-Based Multicriteria Fuzzy Group Decision Making Model for Credit Risk Analysis Introduction Methodology Formulation Experimental Study An Illustrative Numerical Example Empirical Comparisons with Different Credit Datasets Conclusions and Future Directions References Subject Index Biographies of Four Authors of the Book...243