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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 1
126-133

Parallel deep forest algorithm based on Spark and NRSCA strategy

Mao Yimin1,2
Liu Shaofen1
1. School of Information Engineering, Jiangxi University of Science & Technology, Ganzhou Jiangxi 341000, China
2. School of Information Engineering, Shaoguan University, Shaoguan Guangdong 512026, China
DOI: 10.19734/j.issn.1001-3695.2023.05.0196

Abstract

Aiming to address several issues encountered by parallel deep forest algorithms in big data environments, such as excessive redundancy and irrelevant features, low utilization rate of features at both ends, slow model convergence speed, and low parallel efficiency of cascading forests, this paper proposed a parallel deep forest algorithm based on Spark and NRSCA strategy(PDF-SNRSCA). Firstly, the algorithm proposed a feature selection strategy(FS-NRS) based on neighborhood rough sets and Fisher score, which measured the correlation and redundancy of features to effectively reduce the number of redundant and irrelevant features. Secondly, it proposed a scanning strategy based on random selection and equidistant extraction(S-RSEE) to ensure that all features were utilized with the same probability and solved the problem of low utilization rate of two ends in multi-granularing scanning. Finally, combining with the Spark framework, the algorithm realized the parallel trai-ning of cascading forests, and it proposed a feature filtering mechanism based on the importance index(FFM-II) to balance the dimensions of enhanced class vectors and original class vectors, thereby accelerating the model convergence speed. Meanwhile, the algorithm designed a task scheduling mechanism based on SCA(TSM-SCA) to redistribute tasks and ensure load balancing in the cluster, which solved the problem of low parallel efficiency of cascading forests. Experiments show that the PDF-SNRSCA algorithm can effectively improve the classification performance of deep forests and greatly enhance the efficiency of parallel training of deep forests.

Foundation Support

广东省重点提升项目(2022ZDJS048)
韶关市科技项目(220607154531533)
科技创新2030-“新一代人工智能”重大项目(2020AAA0109605)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.05.0196
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 1
Section: Algorithm Research & Explore
Pages: 126-133
Serial Number: 1001-3695(2024)01-019-0126-08

Publish History

[2023-07-21] Accepted Paper
[2024-01-05] Printed Article

Cite This Article

毛伊敏, 刘绍芬. 基于Spark和NRSCA策略的并行深度森林算法 [J]. 计算机应用研究, 2024, 41 (1): 126-133. (Mao Yimin, Liu Shaofen. Parallel deep forest algorithm based on Spark and NRSCA strategy [J]. Application Research of Computers, 2024, 41 (1): 126-133. )

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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