Robust fuzzy rough classifiers

Qinghua Hu; Shuang An; Xiao Yu; Daren Yu

doi:10.1016/j.fss.2011.01.016

Robust fuzzy rough classifiers

Qinghua Hu^*
, Shuang An
, Xiao Yu
, Daren Yu

^*Corresponding author for this work

School of Energy Science and Engineering

Harbin Institute of Technology
Hong Kong Polytechnic University

Research output: Contribution to journal › Article › peer-review

54 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	26-43
Number of pages	18
Journal	Fuzzy Sets and Systems
Volume	183
Issue number	1
DOIs	https://doi.org/10.1016/j.fss.2011.01.016
State	Published - 16 Nov 2011

Keywords

Approximate reasoning
Decision analysis
Fuzzy rough sets
Fuzzy statistics and data analysis
Robustness

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10.1016/j.fss.2011.01.016

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title = "Robust fuzzy rough classifiers",

abstract = "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.",

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author = "Qinghua Hu and Shuang An and Xiao Yu and Daren Yu",

year = "2011",

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AU - Hu, Qinghua

AU - An, Shuang

AU - Yu, Xiao

AU - Yu, Daren

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N2 - 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.

AB - 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.

KW - Approximate reasoning

KW - Decision analysis

KW - Fuzzy rough sets

KW - Fuzzy statistics and data analysis

KW - Robustness

UR - https://www.scopus.com/pages/publications/80052267804

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M3 - 文章

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SP - 26

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JO - Fuzzy Sets and Systems

JF - Fuzzy Sets and Systems

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ER -

Robust fuzzy rough classifiers

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Keywords

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