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App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 11

Rough set attribute reduction algorithm based on differential teaching-learning optimization

Zhou Wanting
Zheng Yingchun
Wei Botao
Xi'an University of Science & Technology School of Science, Xi'an 710054, China
DOI: 10.19734/j.issn.1001-3695.2024.04.0102

Abstract

In order to address the challenges of high computational complexity and the tendency to get stuck in local optima during attribute reduction within traditional Rough Set Theory, this paper proposes an innovative Rough Set Attribute Reduction Algorithm Based on Differential Teaching-learning Optimization (AR-DTLBO) . Leveraging the global search capabilities of the Differential Teaching-learning Optimization algorithm along with the strengths of Rough Set Theory in handling imprecise and uncertain data, the algorithm aims to optimize the process. Firstly, the Teaching-learning Optimization algorithm is enhanced by introducing an adaptive teaching factor and a differential mutation strategy, thereby bolstering its search capabilities and optimization performance. Subsequently, the attribute reduction process is refined through the improved Teaching-learning Optimization algorithm’s “teaching” and “learning” phases, effectively reducing the dimensionality and complexity of datasets. Finally, comparative experiments were conducted between the proposed AR-DTLBO algorithm and six other algorithms, using eight datasets from the UCI database. The experimental results actively demonstrate that our algorithm achieves favorable outcomes in terms of reduction length, reduction time, reduction rate, and classification accuracy. This successful reduction and optimization of datasets not only reduces redundant information but also enhances the precision of decision rules. These findings provide valuable support for decision analysis, data mining, and other related fields.

Foundation Support

国家自然科学基金资助项目(12001420)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.04.0102
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 11

Publish History

[2024-08-05] Accepted Paper

Cite This Article

周婉婷, 郑颖春, 魏博涛. 融合差分教学优化的粗糙集属性约简算法 [J]. 计算机应用研究, 2024, 41 (11). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.04.0102. (Zhou Wanting, Zheng Yingchun, Wei Botao. Rough set attribute reduction algorithm based on differential teaching-learning optimization [J]. Application Research of Computers, 2024, 41 (11). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.04.0102. )

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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