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
收藏
页码：1523 / 1529
页数：7
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