In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 9
2625-2628,2633

Research on extreme random forest algorithm based on feature selection

Yang Fengrui1a,1b,2
Luo Sifan1a,1b
Li Qianyang1a,1b
1. a. School of Communication & Information Engineering, b. Research Center of New Telecommunication Technology, Chongqing University of Posts & Telecommunications, Chongqing 400065, China
2. Chongqing University of Posts & Telecommunications Information Technology(Group)Co. Ltd. , Chongqing 401121, China
DOI: 10.19734/j.issn.1001-3695.2019.04.0122

Abstract

High-dimensional complex data processing is an important problem in the field of data mining. Aiming at the pro-blems of imbalance of prediction accuracy and low overall classification efficiency of existing feature selection integrated classification algorithms, this paper proposed a feature selection classification algorithm combining probability correlation and extreme random forest. The algorithm used a feature selection method that fully considered the correlation between the features and the P-value, which avoided the redundancy caused by the tree node splitting process; Besides, this algorithm took the random tree as the base classifier and the extreme random forest as the whole framework, which made it achieve higher accuracy and better generalization error. The experimental results show that the algorithm can achieve good results in data set classification accuracy and integrity compared with random forest algorithm and extreme random forest algorithm.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.04.0122
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 9
Section: Algorithm Research & Explore
Pages: 2625-2628,2633
Serial Number: 1001-3695(2020)09-012-2625-04

Publish History

[2020-09-05] Printed Article

Cite This Article

杨丰瑞, 罗思烦, 李前洋. 基于特征选择的极限随机森林算法研究 [J]. 计算机应用研究, 2020, 37 (9): 2625-2628,2633. (Yang Fengrui, Luo Sifan, Li Qianyang. Research on extreme random forest algorithm based on feature selection [J]. Application Research of Computers, 2020, 37 (9): 2625-2628,2633. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)