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Article . 2018 . Peer-reviewed
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Article . 2018
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Computable aggregations

Authors: Javier Montero; Ramón González del Campo; Luis Garmendia; Daniel Gómez 0001; Juan Tinguaro Rodríguez;

Computable aggregations

Abstract

In this paper, we postulate computation as a key element in assuring the consistency of a family of aggregation functions so that such a family of operators can be considered an aggregation rule. In particular, we suggest that the concept of an aggregation rule should be defined from a computational point of view, focusing on the computational properties of such an aggregation, i.e., on the manner in which the aggregation values are computed. The new algorithmic definition of aggregation we propose provides an operational approach to aggregation, one that is based upon lists of variable length and that produces a solution even when portions of data are inserted or deleted. Among other advantages, this approach allows the construction of different classifications of aggregation rules according to the programming paradigms used for their computation or according to their computational complexity.

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Keywords

Probabilidades, aggregation rule, aggregation operator, Reasoning under uncertainty in the context of artificial intelligence, aggregation function

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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
17
Top 10%
Top 10%
Top 10%
Green
bronze