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dc.contributor.author Qu, Yanpeng
dc.contributor.author Shang, Changjing
dc.contributor.author MacParthaláin, Neil
dc.contributor.author Wu, Wei
dc.contributor.author Shen, Qiang
dc.date.accessioned 2018-08-14T18:42:32Z
dc.date.available 2018-08-14T18:42:32Z
dc.date.issued 2018-04-01
dc.identifier.citation Qu , Y , Shang , C , MacParthaláin , N , Wu , W & Shen , Q 2018 , ' Multi-functional nearest-neighbour classification ' Soft Computing , vol. 22 , no. 8 , pp. 2717-2730 . https://doi.org/10.1007/s00500-017-2528-4 en
dc.identifier.issn 1432-7643
dc.identifier.other PURE: 10699959
dc.identifier.other PURE UUID: ad5fcc18-b214-430a-85e6-c5aed8b6a1c3
dc.identifier.other Scopus: 85014563024
dc.identifier.other handle.net: 2160/46854
dc.identifier.other ORCID: /0000-0003-1935-2914/work/60054314
dc.identifier.other ORCID: /0000-0002-2441-713X/work/61835734
dc.identifier.uri http://hdl.handle.net/2160/46854
dc.description.abstract The k nearest-neighbour (kNN) algorithm has enjoyed much attention since its inception as an intuitive and effective classification method. Many further developments of kNN have been reported such as those integrated with fuzzy sets, rough sets, and evolutionary computation. In particular, the fuzzy and rough modifications of kNN have shown significant enhancement in performance. This paper presents another significant improvement, leading to a multi-functional nearest-neighbour (MFNN) approach which is conceptually simple to understand. It employs an aggregation of fuzzy similarity relations and class memberships in playing the critical role of decision qualifier to perform the task of classification. The new method offers important adaptivity in dealing with different classification problems by nearest-neighbour classifiers, due to the large and variable choice of available aggregation methods and similarity metrics. This flexibility allows the proposed approach to be implemented in a variety of forms. Both theoretical analysis and empirical evaluation demonstrate that conventional kNN and fuzzy nearest-neighbour, as well as two recently developed fuzzy-rough nearest-neighbour algorithms can be considered as special cases of MFNN. Experimental results also confirm that the proposed approach works effectively and generally outperforms many state-of-the-art techniques en
dc.format.extent 14 en
dc.language.iso eng
dc.relation.ispartof Soft Computing en
dc.rights en
dc.subject aggregation en
dc.subject classification en
dc.subject nearest-neighbour en
dc.subject similarity relation en
dc.title Multi-functional nearest-neighbour classification en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.description.version publishersversion en
dc.identifier.doi https://doi.org/10.1007/s00500-017-2528-4
dc.contributor.institution Department of Computer Science en
dc.contributor.institution IMPACS en
dc.description.status Peer reviewed en


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