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A Review of the OWA Determination Methods: Classification and Some Extensions

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Recent Developments in the Ordered Weighted Averaging Operators: Theory and Practice

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 265))

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Abstract

The OWA operator determination is an important prerequisite step for OWA operator applications. With the application of OWA operator in various areas, the OWA operator determination becomes an active topic in recent years. Based on recent developments, the paper give a summary on the OWA determination methods in classification way: the optimization criteria methods, the sample learning methods, the function based methods, the argument dependent methods and the preference methods. Some relationships between the methods in the same kind and the relationships between different kinds are provided. An uniform framework to connect these OWA determination methods together is also attempted. Some extensions, problems and future research directions are given with discussions.

The work is supported by the National Natural Science Foundation of China (NSFC) under project 70771025, and Program for New Century Excellent Talents in University of China NCET-06-0467.

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  1. School of Economics and Management, Southeast University, Nanjing, 210096, Jiangsu, P.R. China

    Xinwang Liu

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Editors and Affiliations

  1. Machine Intelligence Institute , Iona College, 10801, New Rochelle, NY, USA

    Ronald R. Yager

  2. Systems Research Institute , Polish Academy of Sciences, Ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

  3. School of Information Technology , Deakin University, 221 Burwood Highway, 3125, Burwood, Victoria, Australia

    Gleb Beliakov

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Liu, X. (2011). A Review of the OWA Determination Methods: Classification and Some Extensions. In: Yager, R.R., Kacprzyk, J., Beliakov, G. (eds) Recent Developments in the Ordered Weighted Averaging Operators: Theory and Practice. Studies in Fuzziness and Soft Computing, vol 265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17910-5_4

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  • DOI: https://doi.org/10.1007/978-3-642-17910-5_4

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