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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 9

Semi-supervised ordinal classification method based on ordinal proxies and dual-confidence filtering

Chen Jinxiang
Tang Mengzi
Xie Qing
Liu Yongjian
Wuhan University of Technology, College of Computer & Artificial Intelligence, Wuhan 430070, China
DOI: 10.19734/j.issn.1001-3695.2025.03.0046

Abstract

This study proposed a semi-supervised classification framework (OPMatch) integrating ordinal proxies and dual-confidence filtering to address insufficient ordinal categorical relationship modeling and pseudo-label noise in current semi-supervised methods for ordinal classification tasks. First, it introduced an ordinal unimodal constraint term into the classification cross-entropy loss. This term enhanced model sensitivity to misclassification costs through unimodal distribution constraints. Second, the method established an ordinal semicircular proxy mechanism on unit hyperspheres of feature spaces. This mechanism effectively models category ordinal relationships under data scarcity. For unlabeled data, the framework employed data augmentation and consistency regularization combined with a dual-confidence filtering strategy to generate pseudo-labels, effectively leveraging unlabelled data while mitigating pseudo-label noise impacts. Experiments on three ordinal classification datasets (Adience, Aptos2019, and HistoricalColor) demonstrated superior performance compared to existing semi-supervised methods. The framework achieves significant accuracy improvements in label-scarce scenarios. It provides a universal yet effective approach for semi-supervised ordinal classification tasks.

Foundation Support

武汉理工大学自主创新研究基金项目(2024IVA036)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.03.0046
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 9

Publish History

[2025-05-21] Accepted Paper

Cite This Article

陈锦翔, 汤梦姿, 解庆, 等. 基于有序代理与双置信筛选的半监督有序分类方法 [J]. 计算机应用研究, 2025, 42 (9). (2025-05-27). https://doi.org/10.19734/j.issn.1001-3695.2025.03.0046. (Chen Jinxiang, Tang Mengzi, Xie Qing, et al. Semi-supervised ordinal classification method based on ordinal proxies and dual-confidence filtering [J]. Application Research of Computers, 2025, 42 (9). (2025-05-27). https://doi.org/10.19734/j.issn.1001-3695.2025.03.0046. )

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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