Multicriteria decision aid classification methods
By: by Michael Doumpos and Constantin Zopounidis.
Contributors: Zopounidis, Constantin.
Series: Applied optimization -- v. 73
Publisher: Kluwer Academic Publishers
Published: 2002
Place of publication: Dordrecht; Boston
Language: eng
Number of pages: 252
Subjects: Multiple criteria decision making.; Classification -- Mathematical models.
Description: The book discusses a new approach to the classification problem following the decision support orientation of multicriteria decision aid. The book reviews the existing research on the development of classification methods, investigating the corresponding model development procedures, and providing a thorough analysis of their performance both in experimental situations and real-world problems from the field of finance. Audience: Researchers and professionals working in management science, decision analysis, operations research, financial/banking analysis, economics, statistics, computer science, as well as graduate students in management science and operations research.
Notes: Includes bibliographical references (p. [233]-250) and index.
Table of contents: title: Machine generated contents note: CHAPTER 1: INTRODUCTION TO THE CLASSIFICATION PROBLEM 1. Decision making problematics 2. The classification problem 3. General outline of classification methods 4. The proposed methodological approach and the objectives of the book CHAPTER 2: REVIEW OF CLASSIFICATION TECHNIQUES 1. Introduction 2. Statistical and econometric techniques 2.1 Discriminant analysis 2.2 Logit and probit analysis 3. Non-parametric techniques 3.1 Neural networks 3.2 Machine learning 3.3 Fuzzy set theory 3.4 Rough sets CHAPTER 3: MULTICRITERIA DECISION AID CLASSIFICATION TECHNIQUES 1. Introduction to multicriteria decision aid 1.1 Objectives and general framework 1.2 Brief historical review 1.3 Basic concepts 2. Methodological approaches 2.1 Multiobjective mathematical programming 2.2 Multiattribute utility theory 2.3 Outranking relation theory 2.4 Preference disaggregation analysis 3. MCDA techniques for classification problems 3.1 Techniques based on the direct interrogation of the decision maker 3.1.1 The AHP method 3.1.2 The ELECTRE TRI method 3.1.3 Other outranking classification methods 3.2 The preference disaggregation paradigm in classification problems CHAPTER 4: PREFERENCE DISAGGREGATION CLASSIFICATION METHODS 1. Introduction 2. The UTADIS method 2.1 Criteria aggregation model 2.2 Model development process 2.2.1 General framework 2.2.2 Mathematical formulation 2.3 Model development issues 2.3.1 The piece-wise linear modeling of marginal utilities 2.3.2 Uniqueness of solutions 3. The multi-group hierarchical discrimination method (MHDIS) 3.1 Outline and main characteristics 3.2 The hierarchical discrimination process 3.3 Estimation of utility functions 3.4 Model extrapolation Appendix: Post optimality techniques for classification model development in the UTADIS method CHAPTER 5: EXPERIMENTAL COMPARISON OF CLASSIFICATION TECHNIQUES 1. Objectives 2. The considered methods 3. Experimental design 2.1 The factors 2.2 Data generation procedure 4. Analysis of results 5. Summary of major findings Appendix: Development of ELECTRE TRI classification models using a preference disaggregation approach CHAPTER 6: CLASSIFICATION PROBLEMS IN FINANCE 1. Introduction 2. Bankruptcy prediction 2.1 Problem domain 2.2 Data and methodology 2.3 The developed models 2.3.1 The model of the UTADIS method 2.3.2 The model of the MHDIS method 2.3.3 The ELECTRE TRI model 2.3.4 The rough set model 2.3.5 The statistical models 2.4 Comparison of the bankruptcy prediction models 3. Corporate credit risk assessment 3.1 Problem domain 3.2 Data and methodology 3.3 The developed models 3.3.1 The UTADIS model 3.3.2 The model of the MHDIS method 3.3.3 The ELECTRE TRI model 3.3.4 The rough set model 3.3.5 The models of the statistical techniques 3.4 Comparison of the credit risk assessment models 4. Stock evaluation 4.1 Problem domain 4.2 Data and methodology 4.3 The developed models 4.3.1 The MCDA models 4.3.2 The rough set model 4.4 Comparison of the stock evaluation models CHAPTER 7: CONCLUSIONS AND FUTURE PERSPECTIVES 1. Summary of main findings 2. Issues for future research REFERENCES SUBJECT INDEX.