Robust fuzzy rough classifiers

Authors:
Qinghua Hu;Shuang An;Xiao Yu;Daren Yu
Affiliations:
Harbin Institute of Technology, Harbin 150001, China and Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China;Harbin Institute of Technology, Harbin 150001, China;Harbin Institute of Technology, Harbin 150001, China;Harbin Institute of Technology, Harbin 150001, China
Venue:
Fuzzy Sets and Systems
Year:
2011

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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.