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. 38, 2021 No. 5
1398-1402

Clustering under-sampling weighted random forest algorithm for processing unbalanced data

Wang Lei
Liu Yu
Liu Zhizhong
Qi Junyan
School of Computer Science & Technology, Henan Polytechnic University, Jiaozuo Henan 454003, China
DOI: 10.19734/j.issn.1001-3695.2020.05.0128

Abstract

Aiming at the problems of lack of representativeness and poor classification performance in existing under-sampling processing algorithms, this paper proposed a clustering based weighted random forest algorithm based on clustering undersampling(CUS-WRF). The method used K-means algorithm to the cluster the majority samples, and introduced the Euclidean distance as the weight basis for assigning the number of samples when under-sampling, so that the sampled majority samples and minority samples formed a balanced sample set. The tree was the basic classifier, the weighted random forest was the overall framework. At the same time, the results used the accuracy of the test sample as the weight of each tree to complete the voting. It effectively improved the overall classification performance. The experiment selected eight groups of KEEL data sets. The results show that the CUS-WRF algorithm is more advantageous in classification performance and stability than the other four unbalanced data processing algorithms based on random forests.

Foundation Support

国家自然基金面上项目(61872126)
河南省重点科技攻关项目(192102210123)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.05.0128
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 5
Section: Algorithm Research & Explore
Pages: 1398-1402
Serial Number: 1001-3695(2021)05-021-1398-05

Publish History

[2021-05-05] Printed Article

Cite This Article

王磊, 刘雨, 刘志中, 等. 处理不平衡数据的聚类欠采样加权随机森林算法 [J]. 计算机应用研究, 2021, 38 (5): 1398-1402. (Wang Lei, Liu Yu, Liu Zhizhong, et al. Clustering under-sampling weighted random forest algorithm for processing unbalanced data [J]. Application Research of Computers, 2021, 38 (5): 1398-1402. )

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)