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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 3
750-759,765

Parallel random forest algorithm combining gain ratio and stacked auto encoders

Liu Weiming1a,1b
Chen Weida1a
Mao Yimin1a
Chen Zhigang2
1. a. School of Information Engineering, b. School of Resource & Environmental Engineering, Jiangxi University of Science & Technology, Ganzhou Jiangxi 341000, China
2. School of Computer Science & Engineering, Central South University, Changsha 410083, China
DOI: 10.19734/j.issn.1001-3695.2022.08.0374

Abstract

In the big data environment, the random forest algorithm suffers from excessive redundancy and irrelevant features, the insufficient spatial information content of feature subspace, and low parallelization efficiency. To resolve these issues, this paper presented PRFGRSAE. Firstly, this algorithm proposed a DRNGRSAE, which filtered redundant and irrelevant features of the feature set and extracted features by stacked auto-encoders to reduce the number of redundant and irrelevant features effectively. Secondly, it proposed a SSLF that combined Latin hypercube sampling and normalized correlation degree, which formed feature subspaces with high spatial expression by performing multi-layer division sampling on the feature set, and ensured the feature subspace information content. Finally, it proposed a reducer allocation strategy DSVLA combining with variable action learning automata, which allocated each cluster to reducers for processing evenly and improved the parallelization efficiency effectively. Experimental results show that compared with IMRF, KSMRF, and GAPRF algorithms, the speedup ratio and accuracy of the PRFGRSAE algorithm are significantly improved. Therefore, the algorithm can obtain higher accuracy and parallel efficiency when applied to process large data, especially for data sets with more features.

Foundation Support

2020年度科技创新2030—“新一代人工智能”重大项目(2020AAA0109605)
国家自然科学基金资助项目(41562019)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.08.0374
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 3
Section: Algorithm Research & Explore
Pages: 750-759,765
Serial Number: 1001-3695(2023)03-017-0750-10

Publish History

[2022-10-14] Accepted Paper
[2023-03-05] Printed Article

Cite This Article

刘卫明, 陈伟达, 毛伊敏, 等. 结合增益率与堆叠自编码器的并行随机森林算法 [J]. 计算机应用研究, 2023, 40 (3): 750-759,765. (Liu Weiming, Chen Weida, Mao Yimin, et al. Parallel random forest algorithm combining gain ratio and stacked auto encoders [J]. Application Research of Computers, 2023, 40 (3): 750-759,765. )

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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