Robust fuzzy rough classifiers |
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Authors: | Qinghua Hu Shuang AnXiao Yu Daren Yu |
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Institution: | a Harbin Institute of Technology, Harbin 150001, China b Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China |
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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. |
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Keywords: | Approximate reasoning Decision analysis Fuzzy statistics and data analysis Fuzzy rough sets Robustness |
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