Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 37, Issue 1, Pages 360-365Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2009.05.039
Keywords
Composite indicator (CI); Multiple criteria decision analysis (MCDA); Aggregation; Distance; Entropy
Categories
Funding
- National Natural Science Foundation of China [70873058]
- National Social Science Foundation of China [08ZD046]
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Composite indicators (CIs) have been widely accepted as a useful tool for performance comparisons, public communication and decision support in a wide spectrum of fields, e.g. economy, environment and knowledge/information/innovation. The quality and reliability of a Cl depend heavily on the underlying construction scheme where data aggregation is a major step, This paper analyzes the data aggregation problem in Cl construction from the point of view of information loss. Based on the minimum information loss principle, the distance-based and entropy-based aggregation models for constructing CIs are presented. The entropy-based aggregation model has also been extended to deal with qualitative data. It is shown that the proposed aggregation models have close relationships with several popular MCDA aggregation methods in Cl construction, although our proposed models seem to be more flexible while more complex in application. Two case studies are presented to illustrate the use of the proposed aggregation models. (C) 2009 Elsevier Ltd. All rights reserved.
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