Optimizing Random Forest with Apache Spark: A Survey on Distributed Machine Learning and Big Data Scalability

Zixiang Yan

doi:10.47297/taposatWSP2633-456911.20250605

PDF
Export
Share
Collection
Album

Optimizing Random Forest with Apache Spark: A Survey on Distributed Machine Learning and Big Data Scalability

Volume 6, Issue 5

Views: 0 Downloads: 0

Optimizing Random Forest with Apache Spark: A Survey on Distributed Machine Learning and Big Data Scalability
Optimizing Random Forest with Apache Spark: A Survey on Distributed Machine Learning and Big Data Scalability
York University, Faculty of Science, Toronto, Ontario, Canada, YO10 5DD
Vol. 6, Issue 5, Pages: 49-53(2025)
Published：19 May 2025
DOI：10.47297/taposatWSP2633-456911.20250605
Accepted：

Scan QR Code

Yan Zixiang.Optimizing Random Forest with Apache Spark: A Survey on Distributed Machine Learning and Big Data Scalability[J].Theory and Practice of Science and Technology,2025,06(05):49-53.
DOI：

Yan Zixiang.Optimizing Random Forest with Apache Spark: A Survey on Distributed Machine Learning and Big Data Scalability[J].Theory and Practice of Science and Technology,2025,06(05):49-53. DOI： 10.47297/taposatWSP2633-456911.20250605.

摘要

Abstract

Current trends have contributed to the rapid development of Big Data Processing Technologies. As one of the first variants of machine learning algorithms

random forests have exceptional classification and elaboration characteristics. The field of application in many applications has gradually expanded

and traditional random forests have been threatened

especially in large-scale data set processing. The scope of application is limited

and high computational costs and long training time are especially important. As a distributed computing-based platform in RAM

Apache Spark can optimize the learning speed and cost of developing random noise algorithms in a big data environment. This article discusses the random noise optimization strategy based on Apache Spark.it not only deals with parallel computing

but also discusses the techniques of object selection optimization and model improvement. At the same time

it outlines the latest achievements in the field of random forest optimization based on Spark and considers possible directions of its further development.

关键词

Keywords

references

Bai, X., Feng, Y., & Li, L. (2021). Optimized random forest prediction model for urban road congestion. Science Technology and Engineering , 21(26), 11205–11211.

Duan, W., & Tong, M. (2021). An improved random forest algorithm based on Spark. Computer Applications and Software , 38(8), 41–50.

Hu, T. (2019). Research on the optimization and parallelization of random forest algorithm based on Spark (Master's thesis). Qilu University of Technology.

Lei, C. (2021). Research on parallel random forest algorithm based on Spark (Master's thesis). Jiangxi University of Science and Technology.

Meng, X., Liu, C., & Cao, Y. (2024). Research on spatiotemporal evolution of urban agglomeration flood disaster resilience based on optimized random forest algorithm. Journal of Disaster Prevention and Mitigation Engineering , 44(4), 784–792.

Yang, J., Xu, J., Yue, Q., & Zeng, D. (2021). Research on SMS fraud user identification based on Spark and random forest. Computer Engineering and Science , 47(5), 23–31.

Yang, Y., & Guo, J. (2021). Optimization of diabetes prediction based on Spark and random forest. Electronics World , 17, 200–201.

Alert me when the article has been cited

Submit

A Literature Review on Spark-Based Optimization of Machine Learning Algorithms for Cyber Fraud Detection

Research and Optimization of Distributed Clustering Algorithms for High-Dimensional Data

Related Author

Junyi Zou Ludwig-Maximilians-Universität(LMU), Munich, Bayern

Runfeng Zou Queen's University of Kingston

Related Institution

Ludwig-Maximilians-Universität(LMU), Munich, Bayern

Queen's University of Kingston

⁰