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Idempotent Weighted Aggregation Based on Binary Aggregation Trees
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DOI：10.1002/int.21828.png)
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Abstract
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We propose weighted aggregation algorithms for creating general idempotent weighted aggregators of n variables derived from related symmetric idempotent aggregators of two variables. This computational method, together with interpolative aggregation, can be used for the development of general idempotent logic aggregators that satisfy a variety of conditions necessary for building decision models in the area of weighted compensative logic.
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