Kernel-Based Fuzzy-Rough Nearest Neighbour Classification

Yanpeng Qu; Changjing Shang; Qiang Shen; Neil Seosamh MacParthaláin; Wei Wu

doi:10.1109/FUZZY.2011.6007401

Kernel-Based Fuzzy-Rough Nearest Neighbour Classification

Yanpeng Qu, Changjing Shang, Qiang Shen, Neil Seosamh MacParthaláin, Wei Wu

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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.

Iaith wreiddiol	Saesneg
Teitl	Proceedings of the 20th IEEE International Conference on Fuzzy Systems
Cyhoeddwr	IEEE Press
Tudalennau	1523-1529
Nifer y tudalennau	7
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1109/FUZZY.2011.6007401
Statws	Cyhoeddwyd - 06 Medi 2011
Digwyddiad	Fuzzy Systems - Taipei, Taiwan Hyd: 27 Meh 2011 → 30 Meh 2011 Rhif y gynhadledd: 20

Cynhadledd

Cynhadledd	Fuzzy Systems
Teitl cryno	FUZZ-IEEE-2011
Gwlad/Tiriogaeth	Taiwan
Dinas	Taipei
Cyfnod	27 Meh 2011 → 30 Meh 2011

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10.1109/FUZZY.2011.6007401

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@inproceedings{2fe3944c80e4432d9cdb185d49d43d1d,

title = "Kernel-Based Fuzzy-Rough Nearest Neighbour Classification",

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.",

author = "Yanpeng Qu and Changjing Shang and Qiang Shen and MacParthal{\'a}in, {Neil Seosamh} and Wei Wu",

note = "Y. Qu, C. Shang, Q. Shen, N. Mac Parthal{\'a}in, 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; Fuzzy Systems, FUZZ-IEEE-2011 ; Conference date: 27-06-2011 Through 30-06-2011",

year = "2011",

month = sep,

day = "6",

doi = "10.1109/FUZZY.2011.6007401",

language = "English",

pages = "1523--1529",

booktitle = "Proceedings of the 20th IEEE International Conference on Fuzzy Systems",

publisher = "IEEE Press",

address = "United States of America",

}

TY - GEN

T1 - Kernel-Based Fuzzy-Rough Nearest Neighbour Classification

AU - Qu, Yanpeng

AU - Shang, Changjing

AU - Shen, Qiang

AU - MacParthaláin, Neil Seosamh

AU - Wu, Wei

N1 - Conference code: 20

PY - 2011/9/6

Y1 - 2011/9/6

N2 - 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.

AB - 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.

U2 - 10.1109/FUZZY.2011.6007401

DO - 10.1109/FUZZY.2011.6007401

M3 - Conference Proceeding (Non-Journal item)

SP - 1523

EP - 1529

BT - Proceedings of the 20th IEEE International Conference on Fuzzy Systems

PB - IEEE Press

T2 - Fuzzy Systems

Y2 - 27 June 2011 through 30 June 2011

ER -

Kernel-Based Fuzzy-Rough Nearest Neighbour Classification

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