Robust fuzzy rough classifiers

被引：54
作者：
Hu, Qinghua [1 ,2 ]
An, Shuang [1 ]
Yu, Xiao [1 ]
Yu, Daren [1 ]
机构：
[1] Harbin Inst Technol, Harbin 150001, Peoples R China
[2] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China
来源：
FUZZY SETS AND SYSTEMS | 2011年 / 183卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Approximate reasoning; Decision analysis; Fuzzy statistics and data analysis; Fuzzy rough sets; Robustness; ATTRIBUTE REDUCTION; MARGIN CLASSIFIERS; CLASSIFICATION; ALGORITHMS; APPROXIMATION; OUTLIERS; MACHINE;
D O I：
10.1016/j.fss.2011.01.016
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Fuzzy rough sets, generalized from Pawlak's rough sets, were introduced for dealing with continuous or fuzzy data. This model has been widely discussed and applied these years. It is shown that the model of fuzzy rough sets is sensitive to noisy samples, especially sensitive to mislabeled samples. As data are usually contaminated with noise in practice, a robust model is desirable. We introduce a new model of fuzzy rough set model, called soft fuzzy rough sets, and design a robust classification algorithm based on the model. Experimental results show the effectiveness of the proposed algorithm. (C) 2011 Elsevier B.V. All rights reserved.
引用
收藏
页码：26 / 43
页数：18
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