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To solve this problem, this paper proposes a robust fuzzy rough set model (RS\u2010FRS) based on representative samples. Firstly, the fuzzy membership degree of the samples is defined to reflect its fuzziness and uncertainty, and RS\u2010FRS model is constructed to reduce the influence of the noise samples. RS\u2010FRS model does not need to set parameters for the model in advance and can effectively reduce the complexity of the model and human intervention. On this basis, the related properties of RS\u2010FRS model are studied, and the sample pair selection algorithm (SPS) based on RS\u2010FRS is used for feature selection. In this paper, RS\u2010FRS is tested and analysed on the open 12 datasets. The experimental results show that RS\u2010FRS model proposed can effectively select the most relevant features and has certain robustness to the noise information. The proposed model has a good applicability for data processing and can effectively improve the performance of feature selection.<\/jats:p>","DOI":"10.1155\/2021\/6685396","type":"journal-article","created":{"date-parts":[[2021,1,31]],"date-time":"2021-01-31T21:20:05Z","timestamp":1612128005000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Novel Robust Fuzzy Rough Set Model for Feature 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