Show simple item record

dc.contributor.author Su, Pan
dc.contributor.author Chen, Tianhua
dc.contributor.author Shang, Changjing
dc.contributor.author Shen, Qiang
dc.date.accessioned 2016-03-22T19:39:56Z
dc.date.available 2016-03-22T19:39:56Z
dc.date.issued 2014
dc.identifier.citation Su , P , Chen , T , Shang , C & Shen , Q 2014 , Nearest neighbour-guided induced OWA and its application to journal ranking . in 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) . IEEE Press , pp. 1794-1800 , 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014 , Beijing , United Kingdom of Great Britain and Northern Ireland , 06/07/2014 . en
dc.identifier.citation conference en
dc.identifier.isbn 978-1-4799-2073-0
dc.identifier.other PURE: 6852059
dc.identifier.other PURE UUID: 269beb5d-82e2-4e36-aa07-18555d71b98b
dc.identifier.other Scopus: 84912520774
dc.identifier.other handle.net: 2160/38784
dc.identifier.uri http://hdl.handle.net/2160/38784
dc.description.abstract 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. en
dc.language.iso eng
dc.publisher IEEE Press
dc.relation.ispartof 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) en
dc.rights en
dc.title Nearest neighbour-guided induced OWA and its application to journal ranking en
dc.contributor.institution Department of Computer Science en


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search Cadair


Advanced Search

Browse

Statistics