Attribute Reduction in a Hybrid Decision Information System Based on Fuzzy Conditional Information Entropy Using Iterative Model and Matrix Operation
文献类型：期刊论文
| 作者 | Ma, Xiaoqin1,6; Peng, Yichun4,5; Yu, Wenchang6; Xu, Yi2; Zhang, Qinli6; Li, Zhaowen3 |
| 刊名 | COGNITIVE COMPUTATION
 |
| 出版日期 | 2025-02-01 |
| 卷号 | 17期号:1页码:20 |
| 关键词 | HDISs RST Attribute reduction Fuzzy conditional information entropy Difference matrix |
| ISSN号 | 1866-9956 |
| DOI | 10.1007/s12559-024-10400-2 |
| 英文摘要 | Attribute reduction of hybrid decision information systems (HDISs) is a significant research area within the field of machine learning. Due to the presence of nominal attributes, it is difficult to accurately measure the distance between objects in HDISs, which often results in poor attribute reduction for these systems. Rough set theory (RST) is a crucial tool for attribute reduction, but it requires computation of upper and lower approximations, which often leads to computational difficulties. In response to the aforementioned issues, this paper proposes a fast attribute reduction algorithm for HDISs based on fuzzy conditional information entropy that utilizes an iterative model and matrix operations. Firstly, a novel measurement of the distance between nominal attribute values is defined using decision attributes. Subsequently, fuzzy conditional information entropy is calculated from the perspective of "the attribute values is fed back to the attribute set" and its properties are provided. Additionally, an iterative attribute reduction model and difference matrix are established, and two new matrix operations are introduced. Finally, an iterative attribute reduction algorithm is provided. The results of experiments and statistical tests on fifteen UCI datasets, including three large datasets, demonstrate that the proposed algorithm is more effective and efficient than nine state-of-the-art algorithms. This paper not only addresses the issue of difficulty in measuring the distance between nominal attribute values but also significantly improves the computational efficiency of attribute reduction algorithms based on RST, making it possible for them to be applied to large datasets. |
| WOS研究方向 | Computer Science ; Neurosciences & Neurology |
| 语种 | 英语 |
| WOS记录号 | WOS:001406049300001 |
| 源URL | [http://ir.gig.ac.cn/handle/344008/82591]  |
| 专题 | 中国科学院广州地球化学研究所 |
| 通讯作者 | Peng, Yichun; Li, Zhaowen |
| 作者单位 | 1.Anhui Educ Big Data Intelligent Percept & Applicat, Chizhou, Anhui, Peoples R China 2.Anhui Univ, Sch Comp Sci & Technol, Hefei 23601, Anhui, Peoples R China 3.Putian Univ, Fujian Key Lab Financial Informat Proc, Key Lab Appl Math Fujian Prov Univ, Putian 351100, Fujian, Peoples R China 4.Chinese Acad Sci, Guangzhou Inst Geochem, Guangzhou 510640, Guangdong, Peoples R China 5.Yulin Normal Univ, Sch Comp Sci & Engn, Yulin 537000, Guangxi, Peoples R China 6.Chizhou Univ, Anhui Prov Joint Construct Key Lab Intelligent Edu, Chizhou 247000, Anhui, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ma, Xiaoqin,Peng, Yichun,Yu, Wenchang,et al. Attribute Reduction in a Hybrid Decision Information System Based on Fuzzy Conditional Information Entropy Using Iterative Model and Matrix Operation[J]. COGNITIVE COMPUTATION,2025,17(1):20. |
| APA | Ma, Xiaoqin,Peng, Yichun,Yu, Wenchang,Xu, Yi,Zhang, Qinli,&Li, Zhaowen.(2025).Attribute Reduction in a Hybrid Decision Information System Based on Fuzzy Conditional Information Entropy Using Iterative Model and Matrix Operation.COGNITIVE COMPUTATION,17(1),20. |
| MLA | Ma, Xiaoqin,et al."Attribute Reduction in a Hybrid Decision Information System Based on Fuzzy Conditional Information Entropy Using Iterative Model and Matrix Operation".COGNITIVE COMPUTATION 17.1(2025):20. |
入库方式： OAI收割
来源：广州地球化学研究所
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