Inferring robust decision models in multicriteria classification problems: An experimental analysis

被引：32
|
作者：
Doumpos, Michael [1 ]
Zopounidis, Constantin [1 ,2 ]
Galariotis, Emilios [2 ]
机构：
[1] Tech Univ Crete, Financial Engn Lab, Dept Prod Engn & Management, Khania 73100, Greece
[2] Audencia Nantes Sch Management PRES LUNAM, Ctr Financial & Risk Management, F-44312 Nantes, France
来源：
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH | 2014年 / 236卷 / 02期
关键词：
Multiple criteria analysis; Robustness; Disaggregation analysis; Monte Carlo simulation; ADDITIVE VALUE-FUNCTIONS; PREFERENCE DISAGGREGATION; ORDINAL REGRESSION; SORTING PROBLEMS; SET;
D O I：
10.1016/j.ejor.2013.12.034
中图分类号：
C93 [管理学];
学科分类号：
12 ; 1201 ; 1202 ; 120202 ;
摘要：
Recent research on robust decision aiding has focused on identifying a range of recommendations from preferential information and the selection of representative models compatible with preferential constraints. This study presents an experimental analysis on the relationship between the results of a single decision model (additive value function) and the ones from the full set of compatible models in classification problems. Different optimization formulations for selecting a representative model are tested on artificially generated data sets with varying characteristics. (c) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码：601 / 611
页数：11
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