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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 7

Optimization of re-weighted loss function for imbalanced classification under DRO framework

Li Jiajing1
Lin Geng2
1. School of Mathematics & Statistics, Fujian Normal University, Fuzhou Fujian 350117, China
2. School of Computer & Big Data, Minjiang University, Fuzhou Fujian 350108, China
DOI: 10.19734/j.issn.1001-3695.2024.10.0480

Abstract

The skewed distribution of classes often leads classification models to neglect the importance of minority classes, favoring the majority ones, which can render models incapable of accurate classification in multi-class imbalanced tasks. Existing research focuses on the study of data balancing strategies and loss function tuning, ignoring the problem of uncertainty in labelling information, where labels may be wrong or noisy. The uncertainty makes classifier correct classification more challenging. This paper proposed a new loss function, called weighted label distributionally robust Kullback-Leibler, which optimises the predictive distribution under the worst-case scenario, to address the changes and uncertainties in data distribution for the imbalanced classification task. Based on a distributionally robust framework, this approach merges prior information and label weights to focus on minority classes and adapt to label uncertainty. In addition, we propose a simulation method for imbalanced datasets that uses Monte Carlo simulations to provide a more comprehensive evaluation of the performance of each loss function under different classes and at different levels of quantitative variance. Experimental results on simulated, UCI and Kaggle datasets show that the proposed method performs well with imbalanced data and achieves a moderate improvement in Top-k accuracy, F1-scores, precision and recall.

Foundation Support

福建省自然科学基金资助项目(2024J011180)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.10.0480
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 7

Publish History

[2025-03-12] Accepted Paper

Cite This Article

李佳静, 林耿. DRO框架下不平衡分类损失函数重加权优化 [J]. 计算机应用研究, 2025, 42 (7). (2025-03-14). https://doi.org/10.19734/j.issn.1001-3695.2024.10.0480. (Li Jiajing, Lin Geng. Optimization of re-weighted loss function for imbalanced classification under DRO framework [J]. Application Research of Computers, 2025, 42 (7). (2025-03-14). https://doi.org/10.19734/j.issn.1001-3695.2024.10.0480. )

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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