Fuzzy Rough Positive Region based Nearest Neighbour Classification

被引：0

作者：

Verbiest, Nele ^{[1
]}

Cornelis, Chris ^{[1
,2
]}

Jensen, Richard ^{[3
]}

机构：

[1] Univ Ghent, Dept Appl Math & Comp Sci, Ghent, Belgium

[2] Univ Granada, Dept Comp Sci & Artificial Intelligence, Granada, Spain

[3] Aberystwyth Univ, Dept Comp Sci, Aberystwyth SY23 3FG, Dyfed, Wales

来源：

2012 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) | 2012年

关键词：

SETS;

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：

This paper proposes a classifier that uses fuzzy rough set theory to improve the Fuzzy Nearest Neighbour (FNN) classifier. We show that previous attempts to use fuzzy rough set theory to improve the FNN algorithm have some shortcomings and we overcome them by using the fuzzy positive region to measure the quality of the nearest neighbours in the FNN classifier. A preliminary experimental evaluation shows that the new approach generally improves upon existing methods.

引用

页数：7

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