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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 12

Attribute reduction algorithm based on inter-class tolerance classes in incomplete decision systems

Chen Chunyuan
Yin Feng
Wu Liangkun
School of Computer Science & Artificial Intelligence, Southwest Minzu University, Chengdu 610041, China
DOI: 10.19734/j.issn.1001-3695.2025.04.0167

Abstract

Since traditional rough set theory ignores the changes in the relationship between objects in different tolerance classes and across clusters when dealing with incomplete decision systems with missing values, an attribute reduction algorithm based on inter-class tolerance is proposed. Firstly, the domain is divided according to the decision attributes, and the samples within the same decision attribute are defined as a cluster. The concepts of inter-class consistency and discrimination are proposed in the incomplete decision system, and their calculation methods based on inter-class tolerance classes are proposed; secondly, based on the principle of keeping the global inter-class discrimination unchanged, a new standard for evaluating attribute importance is proposed, and the reduction is calculated in combination with a heuristic search strategy; finally, based on nine UCI public data sets, the algorithm is compared with four other attribute reduction algorithms. The results show that the proposed algorithm can effectively remove redundant attributes, and the average classification accuracy is improved by 3.51% compared with the comparison algorithm. Therefore, while ensuring the reduction effect, the algorithm can effectively improve the classification performance of the reduction result.

Foundation Support

西南民族大学中央高校基本科研业务费专项资金优秀学生培养工程项目(ZYN2024113)
国家自然科学基金资助项目(61105061)
成都市哲学社会科学规划资助项目(2022BS027)
西南民族大学中央高校基本科研业务费专项资金资助项目(ZYN2025109)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.04.0167
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 12

Publish History

[2025-08-21] Accepted Paper

Cite This Article

陈春媛, 殷锋, 吴亮昆. 不完备决策系统基于类间容差类的属性约简算法 [J]. 计算机应用研究, 2025, 42 (12). (2025-08-21). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0167. (Chen Chunyuan, Yin Feng, Wu Liangkun. Attribute reduction algorithm based on inter-class tolerance classes in incomplete decision systems [J]. Application Research of Computers, 2025, 42 (12). (2025-08-21). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0167. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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