[1]TANG Yukai,ZHANG Nan,TONG Xiangrong,et al.The multi-class-specific generalized decision preservation reduction in incomplete decision systems[J].CAAI Transactions on Intelligent Systems,2019,14(6):1199-1208.[doi:10.11992/tis.201905059]
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The multi-class-specific generalized decision preservation reduction in incomplete decision systems

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 6 Page number: 1199-1208 Column: 学术论文—机器感知与模式识别 Public date: 2019-11-05
Title:
The multi-class-specific generalized decision preservation reduction in incomplete decision systems
Author(s):
TANG Yukai1; 2;  ZHANG Nan1; 2;  TONG Xiangrong1; 2;  ZHANG Xiaofeng3
1. Key Lab for Data Science and Intelligence Technology of Shandong Higher Education Institutes, Yantai University, Yantai 264005, China;
2. School of Computer and Control Engineering, Yantai University, Yantai 264005, China;
3. School of Information and Electrical Engineering, Ludong University, Yantai 264025, China
Keywords:
rough sets; attribute reduction; incomplete; decision systems; tolerance relation; multi-class-specific; generalized decision preservation reduction; discernibility matrix
CLC:
TP181
DOI:
10.11992/tis.201905059
Abstract:
Attribute reduction has an important place in rough set theory. The method of classical generalized decision preservation reduction in incomplete decision systems is to find the reducts of all decision classes. In practical applications, however, the decision makers may focus on one or several decision classes. To fill this gap, the theoretical framework of multi-class-specific generalized decision preservation reduction in incomplete decision systems is proposed. First, the single-class-specific generalized decision preservation reduction in incomplete decision systems is defined. Related theorems are proposed and proven, and the corresponding discernibility matrix and function are constructed. Then, the single-class-specific generalized decision preservation reduction is extended to the multi-class-specific generalized decision preservation reduction in incomplete decision systems. The algorithm of the multi-class-specific generalized decision preservation reduction based on discernibility matrix in incomplete decision systems (MGDRDM) is proposed. Finally, six datasets from UCI were used for experiments. The experimental results show that when the number of selected specific classes is less than all the decision classes, the average length of reducts will be shortened to varying degrees, the space used will be reduced, and the time efficiency will be roughly improved.
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