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dc.contributor.author Qu, Yanpeng
dc.contributor.author Shang, Changjing
dc.contributor.author Shen, Qiang
dc.contributor.author MacParthaláin, Neil Seosamh
dc.contributor.author Wu, Wei
dc.date.accessioned 2013-08-05T11:34:20Z
dc.date.available 2013-08-05T11:34:20Z
dc.date.issued 2011-09-06
dc.identifier.citation Qu , Y , Shang , C , Shen , Q , MacParthaláin , N S & Wu , W 2011 , Kernel-Based Fuzzy-Rough Nearest Neighbour Classification . in Proceedings of the 20th IEEE International Conference on Fuzzy Systems . IEEE Press , pp. 1523-1529 , Proceedings of the 20th International Conference on Fuzzy Systems (Fuzz-IEEE 2011) , Taipei , Taiwan , 27 Jun 2011 . https://doi.org/10.1109/FUZZY.2011.6007401 en
dc.identifier.citation conference en
dc.identifier.other PURE: 284484
dc.identifier.other PURE UUID: 2fe3944c-80e4-432d-9cdb-185d49d43d1d
dc.identifier.other RAD: 10609
dc.identifier.other DSpace_20121128.csv: row: 4390
dc.identifier.other Scopus: 80053083102
dc.identifier.other handle.net: 2160/11681
dc.identifier.other ORCID: /0000-0003-1935-2914/work/60054309
dc.identifier.other ORCID: /0000-0002-2441-713X/work/61835732
dc.identifier.uri http://hdl.handle.net/2160/11681
dc.description Y. Qu, C. Shang, Q. Shen, N. Mac Parthaláin, W. Wu. Kernel-Based Fuzzy-Rough Nearest Neighbour Classification. Proceedings of the 20th International Conference on Fuzzy Systems (Fuzz-IEEE 2011), pp. 1523-1529, 2011 en
dc.description.abstract Fuzzy-rough sets play an important role in dealing with imprecision and uncertainty for discrete and real-valued or noisy data. However, there are some problems associated with the approach from both theoretical and practical viewpoints. These problems have motivated the hybridisation of fuzzy-rough sets with kernel methods. Existing work which hybridises fuzzy-rough sets and kernel methods employs a constraint that enforces the transitivity of the fuzzy $T$-norm operation. In this paper, such a constraint is relaxed and a new kernel-based fuzzy-rough set approach is introduced. Based on this, novel kernel-based fuzzy-rough nearest-neighbour algorithms are proposed. The work is supported by experimental evaluation, which shows that the new kernel-based methods offer improvements over the existing fuzzy-rough nearest neighbour classifiers. en
dc.format.extent 7 en
dc.language.iso eng
dc.publisher IEEE Press
dc.relation.ispartof Proceedings of the 20th IEEE International Conference on Fuzzy Systems en
dc.rights en
dc.title Kernel-Based Fuzzy-Rough Nearest Neighbour Classification en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontobookanthology/conference en
dc.description.version preprint en
dc.identifier.doi https://doi.org/10.1109/FUZZY.2011.6007401
dc.contributor.institution Department of Computer Science en


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