A relative uncertainty measure for fuzzy rough feature selection
Uncertainty measure is an important tool for data analysis. In practical applications, the collected data are subject to different probability distributions. This requires that the uncertainty measure has generalization performance. Fuzzy rough set (FRS) theory is a popular mathematical tool for unc...
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| Published in | International journal of approximate reasoning Vol. 139; pp. 130 - 142 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.12.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0888-613X 1873-4731 |
| DOI | 10.1016/j.ijar.2021.09.014 |
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| Abstract | Uncertainty measure is an important tool for data analysis. In practical applications, the collected data are subject to different probability distributions. This requires that the uncertainty measure has generalization performance. Fuzzy rough set (FRS) theory is a popular mathematical tool for uncertainty measure, but the theory does not work well for some data distributions. For example, when the class density difference of the data set is large, FRS theory cannot effectively evaluate the classification uncertainty of samples. In this study, we combine the relative measure with the lower approximation of FRSs to propose a relative uncertainty measure which can address the above-mentioned problem. Furthermore, a fuzzy rough feature selection algorithm is designed, and it is mainly used to test the effectiveness and efficiency of the proposed measure. Experimental results demonstrate that the proposed feature selection algorithm has good performance. It indirectly proves that the relative uncertainty measure is effective and efficient in classification tasks. |
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| AbstractList | Uncertainty measure is an important tool for data analysis. In practical applications, the collected data are subject to different probability distributions. This requires that the uncertainty measure has generalization performance. Fuzzy rough set (FRS) theory is a popular mathematical tool for uncertainty measure, but the theory does not work well for some data distributions. For example, when the class density difference of the data set is large, FRS theory cannot effectively evaluate the classification uncertainty of samples. In this study, we combine the relative measure with the lower approximation of FRSs to propose a relative uncertainty measure which can address the above-mentioned problem. Furthermore, a fuzzy rough feature selection algorithm is designed, and it is mainly used to test the effectiveness and efficiency of the proposed measure. Experimental results demonstrate that the proposed feature selection algorithm has good performance. It indirectly proves that the relative uncertainty measure is effective and efficient in classification tasks. |
| Author | An, Shuang Liu, Jiaying Zhao, Suyun Wang, Changzhong |
| Author_xml | – sequence: 1 givenname: Shuang surname: An fullname: An, Shuang email: anshuang503@163.com organization: Northeastern University at Qinhuangdao, Qinhuangdao 066004, China – sequence: 2 givenname: Jiaying surname: Liu fullname: Liu, Jiaying organization: Northeastern University at Qinhuangdao, Qinhuangdao 066004, China – sequence: 3 givenname: Changzhong surname: Wang fullname: Wang, Changzhong organization: Bohai University, Jinzhou 121013, China – sequence: 4 givenname: Suyun surname: Zhao fullname: Zhao, Suyun organization: Renmin University of China, Beijing 100872, China |
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| CitedBy_id | crossref_primary_10_1109_TETCI_2024_3398027 crossref_primary_10_1016_j_knosys_2022_109092 crossref_primary_10_1016_j_ijar_2023_109039 crossref_primary_10_1007_s11227_024_06631_7 crossref_primary_10_1016_j_fss_2024_108916 crossref_primary_10_1016_j_asoc_2024_112572 |
| Cites_doi | 10.1109/TSMC.2016.2574538 10.1007/s00704-019-02954-1 10.1007/s00500-020-05050-z 10.1016/j.fss.2013.06.012 10.1016/j.knosys.2018.04.023 10.1016/j.ins.2008.05.024 10.1016/j.knosys.2018.10.038 10.1016/j.asoc.2020.107064 10.1007/s10489-020-01863-5 10.1016/j.fss.2017.12.012 10.1007/s13042-020-01142-2 10.1080/03081079008935107 10.1016/j.ins.2010.07.010 10.1016/j.fss.2011.01.016 10.1109/TFUZZ.2011.2181180 10.1109/TFUZZ.2019.2955894 10.1109/TFUZZ.2009.2013204 10.1016/j.ins.2014.03.090 10.1016/j.ijar.2017.03.002 10.1109/TKDE.2017.2650906 10.1007/BF01001956 10.1109/TFUZZ.2019.2949765 10.1007/s00500-018-3178-x 10.1007/s40815-020-00849-2 10.1109/TFUZZ.2017.2768044 10.1016/j.ijar.2017.08.004 |
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| Keywords | Relative uncertainty Sample quality Fuzzy rough sets Relative fuzzy dependency Feature selection |
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| SubjectTerms | Feature selection Fuzzy rough sets Relative fuzzy dependency Relative uncertainty Sample quality |
| Title | A relative uncertainty measure for fuzzy rough feature selection |
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