Nearest Neighbour-Guided Induced OWA and its Application to Journal Ranking

Pan Su, Tianhua Chen, Changjing Shang, Qiang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (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.
Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781479920723
Publication statusPublished - 2014
Externally publishedYes

Publication series

NameProceedings of the IEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584


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