Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 10
3047-3052

Application of layer by layer Transformer in class-imbalanced data

Yang Jingdong1
Li Yiwei1
Jiang Biao1
Jiang Quan2
Han Man2
Song Mengge2
1. School of Optical-Electrical & Computer Engineering, University of Shanghai for Science & Technology, Shanghai 200093, China
2. Guang'anmen Hospital, China Academy of Chinese Medical Science, Beijing 100053, China
DOI: 10.19734/j.issn.1001-3695.2023.01.0056

Abstract

In order to solve the problem that class-imbalance data of clinical medical tables tend to have an impact on the model and that the performance of deep learning framework is difficult to match that of traditional machine learning methods when processing scale data tasks, this paper proposed a layer by layer Transformer(LLT) network model based on cascaded under-sampling. LLT deleted the most types of data layer by layer by cascade under-sampling method to achieve the balance of data categories and reduced the impact of class-imbalance data on the classifier. Moreover, LLT used attention mechanism to carry out correlation evaluation on the features of the input data to achieve feature selection, refined the feature extraction abi-lity and improved the model performance. This paper used RA(rheumatoid arthritis) data as test samples. Experimental results show that, on the premise of not changing the sample distribution, the recognition rate of a few categories is increased by 6.1% by the proposed cascade under-sampling method, which is 1.4% and 10.4% higher than that of the commonly used NEARMISS and ADASYN respectively. The accuracy of the RA tabular data and the F1-score index of LLT reach 72.6% and 71.5%, the AUC value is 0.89, the mAP value is 0.79, and the performance exceeds the current mainstream tabular data classification models such as RF, XGBoost and GBDT. This paper also visualized the model process and analyzed the characteristics affecting RA. It has a good guiding significance for the clinical diagnosis of RA.

Foundation Support

国家自然科学基金资助项目(81973749)
中国中医科学院科技创新工程项目(CI2021A01503)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.01.0056
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 10
Section: Algorithm Research & Explore
Pages: 3047-3052
Serial Number: 1001-3695(2023)10-025-3047-06

Publish History

[2023-04-28] Accepted Paper
[2023-10-05] Printed Article

Cite This Article

杨晶东, 李熠伟, 江彪, 等. 逐层Transformer在类别不均衡数据的应用 [J]. 计算机应用研究, 2023, 40 (10): 3047-3052. (Yang Jingdong, Li Yiwei, Jiang Biao, et al. Application of layer by layer Transformer in class-imbalanced data [J]. Application Research of Computers, 2023, 40 (10): 3047-3052. )

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)