Kernel-Based Fuzzy-Rough Nearest Neighbour Classification

被引：0

作者：

Qu, Yanpeng ^{[1
]}

Shang, Changjing ^{[1
]}

Shen, Qiang ^{[1
]}

Mac Parthalain, Neil ^{[1
]}

Wu, Wei ^{[2
]}

机构：

[1] Aberystwyth Univ, Dept Comp Sci, Aberystwyth, Ceredigion, Wales

[2] Dalian Univ Technol, Sch Math Sci, Dalian 116024, Peoples R China

来源：

IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011) | 2011年

关键词：

Fuzzy-rough sets; Fuzzy tolerance relation; Kernel theory; Nearest neighbour classification;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. The abstract goes here.

引用

页码：1523 / 1529

页数：7

共 20 条

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