Applied Optimization

By: Michael Doumpos, Constantin Zopounidis

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Hardcover | 31 August 2002

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276 Pages

Dimensions(cm)
24.13 x 15.88 x 1.91

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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.

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You Can Find This Book In

Non-Fiction Reference, Information & Interdisciplinary Subjects Encyclopaedias & Reference Works Business & Management Management & Management Techniques Management Decision Making Medicine Medicine in General Mathematics Probability & Statistics Operational Research Accounting & Finance

Accounting Finance

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Paperback

Published: 7th December 2010

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Prologue	p. xi
Introduction to the Classification Problem
Decision making problematics	p. 1
The classification problem	p. 4
General outline of classification methods	p. 6
The proposed methodological approach and the objectives of the book	p. 10
Review of Classification Techniques
Introduction	p. 15
Statistical and econometric techniques	p. 15
Discriminant analysis	p. 16
Logit and probit analysis	p. 16
Non-parametric techniques	p. 24
Neural networks	p. 24
Machine learning	p. 27
Fuzzy set theory	p. 30
Rough sets	p. 32
Multicriteria Decision Aid Classification Techniques
Introduction to multicriteria decision aid	p. 39
Objectives and general framework	p. 39
Brief historical review	p. 40
Basic concepts	p. 41
Methodological approaches	p. 43
Multiobjective mathematical programming	p. 45
Multiattribute utility theory	p. 48
Outranking relation theory	p. 50
Preference disaggregation analysis	p. 52
MCDA techniques for classification problems	p. 55
Techniques based on the direct interrogation of the decision maker	p. 55
The AHP method	p. 55
The ELECTRE TRI method	p. 59
Other outranking classification methods	p. 64
The preference disaggregation paradigm in classification problems	p. 66
Preference Disaggregation Classification Methods
Introduction	p. 77
The UTADIS method	p. 78
Criteria aggregation model	p. 78
Model development process	p. 82
General framework	p. 82
Mathematical formulation	p. 86
Model development issues	p. 96
The piece-wise linear modeling of marginal utilities	p. 96
Uniqueness of solutions	p. 97
The multi-group hierarchical discrimination method (MHDIS)	p. 100
Outline and main characteristics	p. 100
The hierarchical discrimination process	p. 101
Estimation of utility functions	p. 105
Model extrapolation	p. 111
Post optimality techniques for classification model development in the UTADIS method	p. 113
Experimental Comparison of Classification Techniques
Objectives	p. 123
The considered methods	p. 124
Experimental design	p. 126
The factors	p. 126
Data generation procedure	p. 131
Analysis of results	p. 134
Summary of major findings	p. 143
Development of ELECTRE TRI classification models using a preference disaggregation approach	p. 150
Classification Problems in Finance
Introduction	p. 159
Bankruptcy prediction	p. 161
Problem domain	p. 161
Data and methodology	p. 164
The developed models	p. 172
The model of the UTADIS method	p. 172
The model of the MHDIS method	p. 174
The ELECTRE TRI model	p. 176
The rough set model	p. 178
The statistical models	p. 179
Comparison of the bankruptcy prediction models	p. 181
Corporate credit risk assessment	p. 185
Problem domain	p. 185
Data and methodology	p. 188
The developed models	p. 194
The UTADIS model	p. 194
The model of the MHDIS method	p. 196
The ELECTRE TRI model	p. 199
The rough set model	p. 200
The models of the statistical techniques	p. 201
Comparison of the credit risk assessment models	p. 202
Stock evaluation	p. 205
Problem domain	p. 205
Data and methodology	p. 209
The developed models	p. 215
The MCDA models	p. 215
The rough set model	p. 220
Comparison of the stock evaluation models	p. 222
Conclusions and Future Perspectives
Summary of main findings	p. 225
Issues for future research	p. 229
References	p. 233
Subject Index	p. 251
Table of Contents provided by Ingram. All Rights Reserved.

Applied Optimization

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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