A Novel Robust Fuzzy Rough Set Model for Feature Selection
The existing fuzzy rough set (FRS) models all believe that the decision attribute divides the sample set into several “clear” decision classes, and this data processing method makes the model sensitive to noise information when conducting feature selection. To solve this problem, this paper proposes...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6685396 |
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| _version_ | 1832554099305873408 |
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| author | Yuwen Li Shoushui Wei Xing Liu Zhimin Zhang |
| author_facet | Yuwen Li Shoushui Wei Xing Liu Zhimin Zhang |
| author_sort | Yuwen Li |
| collection | DOAJ |
| description | The existing fuzzy rough set (FRS) models all believe that the decision attribute divides the sample set into several “clear” decision classes, and this data processing method makes the model sensitive to noise information when conducting feature selection. To solve this problem, this paper proposes a robust fuzzy rough set model (RS-FRS) based on representative samples. Firstly, the fuzzy membership degree of the samples is defined to reflect its fuzziness and uncertainty, and RS-FRS model is constructed to reduce the influence of the noise samples. RS-FRS 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-FRS model are studied, and the sample pair selection algorithm (SPS) based on RS-FRS is used for feature selection. In this paper, RS-FRS is tested and analysed on the open 12 datasets. The experimental results show that RS-FRS 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. |
| format | Article |
| id | doaj-art-8cf940d27c964bbc9d4f7d8628597941 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-8cf940d27c964bbc9d4f7d86285979412025-02-03T05:52:27ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66853966685396A Novel Robust Fuzzy Rough Set Model for Feature SelectionYuwen Li0Shoushui Wei1Xing Liu2Zhimin Zhang3School of Instrument Science and Engineering, Southeast University, Nanjing 210000, ChinaSchool of Control Science and Engineering, Shandong University, Jinan 250100, ChinaDepartment of Anesthesiology, The Third Xiangya Hospital, Central South University, Changsha 410013, ChinaScience and Technology on Information Systems Engineering Laboratory, The 28th Research Institute of CETC, Nanjing 210000, ChinaThe existing fuzzy rough set (FRS) models all believe that the decision attribute divides the sample set into several “clear” decision classes, and this data processing method makes the model sensitive to noise information when conducting feature selection. To solve this problem, this paper proposes a robust fuzzy rough set model (RS-FRS) based on representative samples. Firstly, the fuzzy membership degree of the samples is defined to reflect its fuzziness and uncertainty, and RS-FRS model is constructed to reduce the influence of the noise samples. RS-FRS 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-FRS model are studied, and the sample pair selection algorithm (SPS) based on RS-FRS is used for feature selection. In this paper, RS-FRS is tested and analysed on the open 12 datasets. The experimental results show that RS-FRS 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.http://dx.doi.org/10.1155/2021/6685396 |
| spellingShingle | Yuwen Li Shoushui Wei Xing Liu Zhimin Zhang A Novel Robust Fuzzy Rough Set Model for Feature Selection Complexity |
| title | A Novel Robust Fuzzy Rough Set Model for Feature Selection |
| title_full | A Novel Robust Fuzzy Rough Set Model for Feature Selection |
| title_fullStr | A Novel Robust Fuzzy Rough Set Model for Feature Selection |
| title_full_unstemmed | A Novel Robust Fuzzy Rough Set Model for Feature Selection |
| title_short | A Novel Robust Fuzzy Rough Set Model for Feature Selection |
| title_sort | novel robust fuzzy rough set model for feature selection |
| url | http://dx.doi.org/10.1155/2021/6685396 |
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