Nearest neighbour-guided induced OWA and its application to journal ranking

Pan Su, Tianhua Chen, Changjing Shang, Qiang Shen

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

1 Dyfyniad (Scopus)


Aggregation operators are useful tools which summarise multiple inputs to a single output. In practice, inputs to such operators are variables which represent different criteria, measurements, or opinions from experts. In this paper, a nearest neighbour-guided induced OWA operator, abbreviated as kNN-IOWA, is proposed as a special case of the generic induced OWA where the input arguments are ordered by the average distances to their k nearest neighbours. The weighting vectors in kNN-IOWA are defined, which are used to interpret the overall behaviour of the operator's reliability. kNN-IOWA is applied for building aggregated fuzzy relations between academic journals, based on their indicator scores. It combines the similarities between academic journals to assess their performance with respect to different journal impact indicators. The work is compared against different types of aggregation operator and tested on six bibliometric datasets. The results of experimental evaluation demonstrate that kNN-IOWA outperforms other aggregation operators in terms of standard accuracy and within-1 accuracy. The proposed method also exhibits the advantages of being more intuitive and interpretable.
Iaith wreiddiolSaesneg
Teitl2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
CyhoeddwrIEEE Press
ISBN (Argraffiad)978-1-4799-2073-0
StatwsCyhoeddwyd - 2014
DigwyddiadFuzzy Systems - Beijing, Beijing, Tsieina
Hyd: 06 Gorff 201411 Gorff 2014
Rhif y gynhadledd: 23


CynhadleddFuzzy Systems
Teitl crynoFUZZ-IEEE-2014
Cyfnod06 Gorff 201411 Gorff 2014

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