App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 10

Interpretability enhancement model of random forest using ensemble pruning and multi-objective evolutionary algorithm

Li Yang1
Liao Mengjie1,2
Zhang Jian1,2
1. School of Economics & Management, Beijing Information S&T University, Beijing 100192, China
2. Beijing Key Laboratory of Big Data Decision-making for Green Development, Beijing 100192, China
DOI: 10.19734/j.issn.1001-3695.2024.02.0047

Abstract

Random Forest is a classic black-box model that is widely used in various fields. The structural characteristics of black-box models lead to weak model interpretability, which can be optimized with the help of interpretable techniques to promote the application and development of Random Forest in scenarios with high reliability requirements. This article constructed a rule extraction model based on ensemble pruning and multi-objective evolutionary algorithm. Ensemble pruning is an effective method for solving the problem of extracting rules from tree models that tend to fall into local optima, and multi-objective evolutionary has several applications in balancing rule accuracy and interpretability. This article found that it improved interpretability without sacrificing accuracy. This study integrate ensemble pruning technology with a multi-objective evolutionary algorithm, which enhances the interpretability of random forests and helps promote the decision-making application of this model in areas with high interpretability requirements.

Foundation Support

国家重点研发计划课题(2021YFC3340501)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.02.0047
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 10

Publish History

[2024-07-05] Accepted Paper

Cite This Article

李扬, 廖梦洁, 张健. 利用集成剪枝和多目标优化算法的随机森林可解释增强模型 [J]. 计算机应用研究, 2024, 41 (10). (2024-07-12). https://doi.org/10.19734/j.issn.1001-3695.2024.02.0047. (Li Yang, Liao Mengjie, Zhang Jian. Interpretability enhancement model of random forest using ensemble pruning and multi-objective evolutionary algorithm [J]. Application Research of Computers, 2024, 41 (10). (2024-07-12). https://doi.org/10.19734/j.issn.1001-3695.2024.02.0047. )

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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