Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 6
1743-1748

Research on ensemble learning model for simplified rules and rule reduction strategy

Zhang Weizhi1
Han Xun2,3
Xie Zhiwei4
Shi Shengfei1
1. Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
2. Intelligent Policing Key Laboratory of Sichuan Province, Luzhou Sichuan 646000, China
3. Dept. of Transportation Management, Sichuan Police College, Luzhou Sichuan 646000, China
4. Heilongjiang Agricultural Reclamation Vocational College, Harbin 150025, China
DOI: 10.19734/j.issn.1001-3695.2023.10.0523

Abstract

With the widespread application of machine learning models, researchers have gradually recognized the limitations of such methods. Most of these models are black-box models, resulting in poor interpretability. To address this issue, this paper proposed a rule-based interpretable model and rule reduction method based on ensemble learning models, which included generating optimized random forest models, discovering and reducing redundant rules, and other steps. Firstly, this paper proposed an evaluation method for random forest models, and optimized the key parameters of random forest models based on the idea of reinforcement learning, resulting in a more interpretable random forest model. Secondly, the rule sets extracted from the random forest model were subjected to redundancy elimination, resulting in a more concise rule set. Experimental results on public datasets show that the generated rule sets perform well in terms of prediction accuracy and interpretability.

Foundation Support

智能警务四川省重点实验室课题资助项目(ZNJW2022ZZZD001)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.10.0523
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 6
Section: Algorithm Research & Explore
Pages: 1743-1748
Serial Number: 1001-3695(2024)06-020-1743-06

Publish History

[2024-01-12] Accepted Paper
[2024-06-05] Printed Article

Cite This Article

张纬之, 韩珣, 谢志伟, 等. 面向简化规则的集成学习模型及规则约简策略 [J]. 计算机应用研究, 2024, 41 (6): 1743-1748. (Zhang Weizhi, Han Xun, Xie Zhiwei, et al. Research on ensemble learning model for simplified rules and rule reduction strategy [J]. Application Research of Computers, 2024, 41 (6): 1743-1748. )

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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