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dc.contributor.author Qu, Yanpeng
dc.contributor.author Shang, Changjing
dc.contributor.author Mac Parthaláin, Neil
dc.contributor.author Wu, Wei
dc.contributor.author Shen, Qiang
dc.date.accessioned 2011-09-06T11:03:44Z
dc.date.available 2011-09-06T11:03:44Z
dc.date.issued 2011-09-06
dc.identifier.citation Qu , Y , Shang , C , Mac Parthaláin , N , Wu , W & Shen , Q 2011 , ' Kernel-Based Fuzzy-Rough Nearest Neighbour Classification ' pp. 1523-1529 . en
dc.identifier.other PURE: 178400
dc.identifier.other dspace: 2160/7557
dc.identifier.uri http://hdl.handle.net/2160/7557
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.relation.ispartof en
dc.title Kernel-Based Fuzzy-Rough Nearest Neighbour Classification en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.description.status Non peer reviewed en


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