App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 12

Attribute reduction algorithm based on hybrid granular conditional entropy

Wang Xiaoxue1
Zhang Pengfei2
Tang Minli1
Li Qiuxian1
Wang Guiwen1
1. School of Big Data Engineering, Kaili University, Kaili Guizhou 556011, China
2. School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu Sichuan 611137, China
DOI: 10.19734/j.issn.1001-3695.2026.05.0114

Abstract

To address the issues of single-perspective attribute measurement and low computational efficiency in existing rough set-based attribute reduction methods, this paper proposed an attribute reduction algorithm integrating multi-perspective hybrid entropy and an acceleration strategy. First, it constructed a hybrid granular conditional entropy by integrating information entropy and knowledge granularity as a comprehensive evaluation metric for attribute significance, and theoretically and experimentally verified its monotonicity. Second, the paper demonstrated that this metric preserves the ranking of attribute importance under both positive region-based sample reduction and attribute number reduction. Based on this, it designed a dual acceleration mechanism combining sample and attribute reduction to dynamically reduce the universe size and attribute dimensionality during iteration, thereby speeding up the attribute reduction process. Finally, experimental results on eight UCI datasets show that, compared with four other algorithms, the proposed algorithm achieves an average improvement in computational efficiency of 62.04%, 59.02%, 80.65%, and 86.26%, respectively, and improves the average classification accuracy across three classifiers by 5.77%, 4.34%, and 4.49%. These results indicate that the algorithm not only significantly enhances attribute reduction efficiency but also exhibits good classification performance across different classifiers.

Foundation Support

黔东南州基础研究计划(自然科学)资助项目(黔东南科合基础[2025]0024号)
贵州省基础研究计划(自然科学)青年引导项目(黔科合基础QN[2025]247号)
国家自然科学基金(62406044)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.05.0114
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 12

Publish History

[2026-08-04] Accepted Paper

Cite This Article

王小雪, 张鹏飞, 汤敏丽, 等. 基于混合粒度条件熵的属性约简算法 [J]. 计算机应用研究, 2026, 43 (12). (2026-08-25). https://doi.org/10.19734/j.issn.1001-3695.2026.05.0114. (Wang Xiaoxue, Zhang Pengfei, Tang Minli, et al. Attribute reduction algorithm based on hybrid granular conditional entropy [J]. Application Research of Computers, 2026, 43 (12). (2026-08-25). https://doi.org/10.19734/j.issn.1001-3695.2026.05.0114. )

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.

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