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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 3

Drift detection methods based on chernoff bound

Yang Shurong
Han Meng
Ding Jian
Li Juan
Zhu Shineng
Dai Zhenlong
Yang Wenyan
School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China
DOI: 10.19734/j.issn.1001-3695.2025.07.0254

Abstract

Classification models frequently experience performance degradation due to concept drift in data streams, making timely detection of such drifts critical for updating base classifiers and maintaining high classification accuracy. This paper proposed a novel concept drift detection method (CDDM) based on the Chernoff bound, incorporating two key enhancements to address existing limitations. This paper developed a detection framework grounded in the mean-based Chernoff bound, which is further decomposed into current mean-based and global mean-based Chernoff bounds to achieve an optimal balance between sensitivity and stability. Additionally, this paper designed composite verification strategies that integrate both a dual-check mechanism and a detection-then-verification approach to enhance the robustness and precision of drift identification. To validate the effectiveness of the proposed mechanisms, this paper constructed variant models of CDDM for comparative performance analysis. Experimental results obtained from synthetic datasets demonstrate that CDDM achieves a maximum average accuracy of 85.97% while reducing drift detection delay by 8.80%. These findings confirmed that the proposed method not only enhanced classification accuracy but also accelerated drift detection speed, thereby providing a reliable solution for maintaining the performance of streaming data classification models.

Foundation Support

国家自然科学基金资助项目(62566001)
宁夏自然科学基金资助项目(2025AAC030054)
北方民族大学研究生创新项目(YCX24371)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.07.0254
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 3

Publish History

[2025-11-17] Accepted Paper

Cite This Article

杨书蓉, 韩萌, 丁剑, 等. 基于Chernoff界的概念漂移检测方法 [J]. 计算机应用研究, 2026, 43 (3). (2025-11-18). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0254. (Yang Shurong, Han Meng, Ding Jian, et al. Drift detection methods based on chernoff bound [J]. Application Research of Computers, 2026, 43 (3). (2025-11-18). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0254. )

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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