✦ LIBER ✦

Minimization of uncertainty for ordered weighted average

✍ Scribed by Victor M. Vergara; Shan Xia

Publisher: John Wiley and Sons
Year: 2010
Tongue: English
Weight: 344 KB
Volume: 25
Category: Article
ISSN: 0884-8173
DOI: 10.1002/int.20422

No coin nor oath required. For personal study only.

✦ Synopsis

Existing ordered weighted average (OWA) characterization methods maximize similarity among information sources by seeking maximal weights entropy or by minimizing weights variance. These methods are based solely on the weights, and the uncertainties of input information sources are ignored. However, the purpose of information fusion is to decrease uncertainty and improve data quality. Following this objective, this work proposes a new method to calculate the OWA weights based on the minimization of the aggregated uncertainty. The resulting aggregated value is the most precise, in the sense that any other combination of weights produces larger uncertainty.

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