Attribute reduction in an incomplete categorical decision information system based on fuzzy rough sets
Categorical data is an important class of data in machine learning. Information system based on categorical data is called a categorical information system (CIS), a CIS with missing values is known as an incomplete categorical information system (ICIS) and an ICIS with decision attributes is said to...
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| Published in | The Artificial intelligence review Vol. 55; no. 7; pp. 5313 - 5348 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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Dordrecht
Springer Netherlands
01.10.2022
Springer Springer Nature B.V |
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| Online Access | Get full text |
| ISSN | 0269-2821 1573-7462 |
| DOI | 10.1007/s10462-021-10117-w |
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| Abstract | Categorical data is an important class of data in machine learning. Information system based on categorical data is called a categorical information system (CIS), a CIS with missing values is known as an incomplete categorical information system (ICIS) and an ICIS with decision attributes is said to be an incomplete categorical decision information system (ICDIS). Attribute selection is an important subject in rough set theory. This paper investigates attribute reduction in an ICDIS based on fuzzy rough sets. To depict the similarity for incomplete categorical data, fuzzy symmetry relations in an ICDIS are first introduced. Then, some attribute-evaluation functions, such fuzzy positive regions, dependency function and attribute importance functions are given. Next, the fuzzy-rough iterative computation model for an ICDIS is presented, and an attribute reduction algorithm in an ICDIS based on fuzzy rough sets is given. Finally, experiments are carried out as so to evaluate the performance of the proposed algorithm, and Friedman test and Bonferroni-Dunn test in statistics are conducted. The experimental results indicate that the proposed algorithm is more effective than some existing algorithms. |
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| AbstractList | Categorical data is an important class of data in machine learning. Information system based on categorical data is called a categorical information system (CIS), a CIS with missing values is known as an incomplete categorical information system (ICIS) and an ICIS with decision attributes is said to be an incomplete categorical decision information system (ICDIS). Attribute selection is an important subject in rough set theory. This paper investigates attribute reduction in an ICDIS based on fuzzy rough sets. To depict the similarity for incomplete categorical data, fuzzy symmetry relations in an ICDIS are first introduced. Then, some attribute-evaluation functions, such fuzzy positive regions, dependency function and attribute importance functions are given. Next, the fuzzy-rough iterative computation model for an ICDIS is presented, and an attribute reduction algorithm in an ICDIS based on fuzzy rough sets is given. Finally, experiments are carried out as so to evaluate the performance of the proposed algorithm, and Friedman test and Bonferroni-Dunn test in statistics are conducted. The experimental results indicate that the proposed algorithm is more effective than some existing algorithms. |
| Audience | Academic |
| Author | He, Jiali Chen, Yiying Luo, Damei Qu, Liangdong Wang, Zhihong Wen, Ching-Feng |
| Author_xml | – sequence: 1 givenname: Jiali surname: He fullname: He, Jiali organization: Key Laboratory of Complex System Optimization and Big Data Processing in Department of Guangxi Education, Yulin Normal University – sequence: 2 givenname: Liangdong orcidid: 0000-0002-3838-4594 surname: Qu fullname: Qu, Liangdong email: quliangdong100@126.com organization: School of Artificial Intelligence, Guangxi University for Nationalities – sequence: 3 givenname: Zhihong surname: Wang fullname: Wang, Zhihong organization: Institute of Artificial Intelligence, School of Information Science and Technology, Southwest Jiaotong University – sequence: 4 givenname: Yiying surname: Chen fullname: Chen, Yiying organization: School of Mathematics and Statistics, Minnan Normal University – sequence: 5 givenname: Damei surname: Luo fullname: Luo, Damei organization: School of Mathematics and Information Science, Guangxi University – sequence: 6 givenname: Ching-Feng surname: Wen fullname: Wen, Ching-Feng email: chingfengwen100@126.com, cfwen@kmu.edu.tw organization: Center for Fundamental Science, and Research Center for Nonlinear Analysis and Optimization, Kaohsiung Medical University, Department of Medical Research, Kaohsiung Medical University Hospital |
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| CitedBy_id | crossref_primary_10_1016_j_eswa_2023_121062 crossref_primary_10_3390_sym14071384 crossref_primary_10_1007_s10462_023_10479_3 crossref_primary_10_1016_j_ins_2023_03_027 crossref_primary_10_3390_math12020333 crossref_primary_10_1109_ACCESS_2023_3302527 crossref_primary_10_3390_axioms13110736 crossref_primary_10_3390_sym15030674 crossref_primary_10_1016_j_ins_2024_120900 crossref_primary_10_32604_cmc_2024_057383 |
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| SubjectTerms | Algorithms Artificial Intelligence Computer Science Fuzzy sets Information systems Iterative methods Machine learning Performance evaluation Reduction Rough set models Set theory Statistical tests |
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| Title | Attribute reduction in an incomplete categorical decision information system based on fuzzy rough sets |
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