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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 2
473-481

Parallel deep forest algorithm based on mutual information and mixed weighting

Mao Yimin1,2
Li Wenhao1
1. School of Information Engineering, Jiangxi University of Science & Technology, Ganzhou Jiangxi 341000, China
2. School of Information Engineering, Shaoguan University, Shaoguan Guangdong 512000, China
DOI: 10.19734/j.issn.1001-3695.2023.05.0240

Abstract

In the context of big data environments, the parallel deep forest algorithm faces several challenges, such as an abundance of irrelevant and redundant features, imbalanced multi-granularity scanning, inadequate classification performance, and low parallelization efficiency. To tackle these issues, this paper proposed PDF-MIMW. Firstly, the algorithm introduced FE-MI in the phase of dimensionality reduction, which filtered the original feature set by combining feature importance, interaction, and redundancy metrics, thereby eliminating excessive irrelevant and redundant features. Next, the algorithm proposed an IMGS-P in the phase of multi-granularity scanning, which involved padding the reduced features and performing random sampling on the subsequences obtained after window scanning, thereby ensuring a balanced multi-granularity scanning process. Then, the algorithm put forth the SFC-MW in the phase of cascade forest construction, which utilized the Spark framework to parallelly construct weighted sub-forests, thereby enhancing the model's classification performance. Finally, the algorithm designed a load balancing strategy based on a mixed particle swarm algorithm in the phase of class vector merging, which optimized the load distribution among task nodes in the Spark framework, reducing the waiting time during class vector merging and improving the parallelization efficiency of the model. Experiments demonstrate that the PDF-MIMW algorithm achieves superior classification performance and higher training efficiency in the big data environment.

Foundation Support

广东省重点领域研发计划资助项目(2022B0101020002)
广东省重点提升项目(2022ZDJS048)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.05.0240
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 2
Section: Algorithm Research & Explore
Pages: 473-481
Serial Number: 1001-3695(2024)02-023-0473-09

Publish History

[2023-08-03] Accepted Paper
[2024-02-05] Printed Article

Cite This Article

毛伊敏, 李文豪. 基于互信息和融合加权的并行深度森林算法 [J]. 计算机应用研究, 2024, 41 (2): 473-481. (Mao Yimin, Li Wenhao. Parallel deep forest algorithm based on mutual information and mixed weighting [J]. Application Research of Computers, 2024, 41 (2): 473-481. )

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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