[1]ZHOU Yangyang,QIAN Wenbin,WANG Yinglong,et al.Classification method of cost-sensitive three-way decision boundary region for hybrid data[J].CAAI Transactions on Intelligent Systems,2022,17(2):411-419.[doi:10.11992/tis.202012048]
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Classification method of cost-sensitive three-way decision boundary region for hybrid data

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 2 Page number: 411-419 Column: 学术论文—人工智能基础 Public date: 2022-03-05
Title:
Classification method of cost-sensitive three-way decision boundary region for hybrid data
Author(s):
ZHOU Yangyang1;  QIAN Wenbin1; 2;  WANG Yinglong1;  PENG Lisha3;  ZENG Wuxu1
1. School of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 330045, China;
2. School of Software, Jiangxi Agricultural University, Nanchang 330045, China;
3. School of Engineering Management, Nanjing University, Nanjing 210046, China
Keywords:
three-way decisions; granular computing; cost sensitive; hybrid data; positive domain reduction; boundary region sample processing; rough set; core attribute
CLC:
TP391
DOI:
10.11992/tis.202012048
Abstract:
The research objects of existing three-way decisions models are mostly decision-making systems with single data. Relatively few studies on the boundary region sample processing of mixed data have been conducted. To address this issue, a classification method of a cost-sensitive three-way decision boundary region based on core attributes for hybrid data is proposed in this study. This method computes the core attribute set of the hybrid neighborhood decision system based on positive domain reduction. On this basis, the hybrid neighborhood class is calculated, and the objects are divided into the positive, boundary, and negative regions of each decision-making class through three-way decision rules. The classification method of the three-way decision boundary region based on cost-sensitive learning is proposed. Then, a calculation method of the misclassification cost is constructed to divide the objects in the boundary region. Experiments and analyses are performed on 10 datasets of UCI, which show the feasibility and the effectiveness of the proposed method for the processing of boundary region samples.
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