Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 4
1052-1057

Multi-task ordinal regression with task weight discovery

Zeng Mengyuea
Xiao Yanshana
Liu Bob
a. School of Computer Science & Technology, b. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
DOI: 10.19734/j.issn.1001-3695.2023.08.0376

Abstract

At present, there are only a very few works done on multi-task ordinal regression(OR). These works assume that different tasks contribute equally to the overall model. However, in practice, different tasks may have distinct contributions to the overall model. This paper proposed a novel multi-task ordinal regression method with task weight discovery method. Firstly, it presented a support-vector-machine-based multi-task OR model. By sharing the classifier parameters, the classification information could be transferred among different tasks. Secondly, considering that different tasks had different contributions to the overall model, it assigned each task a weight, which would be automatically optimized during the learning process. Finally, it adopted a heuristic framework to construct the multi-task OR model and optimized the task weights alternately. The experimental results show that the proposed method achieves 3.8% to 12.3% improvements in terms of MZE and 4.1% to 11% improvements in terms of MAE, compared to the existing multi-task OR methods. Considering the different weights of each task, and by automatically optimizing these weights, the proposed method reduces the classification error of the multi-task ordinal regression model.

Foundation Support

国家自然科学基金资助项目(62076074)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.08.0376
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 4
Section: Algorithm Research & Explore
Pages: 1052-1057
Serial Number: 1001-3695(2024)04-014-1052-06

Publish History

[2023-11-03] Accepted Paper
[2024-04-05] Printed Article

Cite This Article

曾梦岳, 肖燕珊, 刘波. 基于任务权重自动优化的多任务序数回归算法 [J]. 计算机应用研究, 2024, 41 (4): 1052-1057. (Zeng Mengyue, Xiao Yanshan, Liu Bo. Multi-task ordinal regression with task weight discovery [J]. Application Research of Computers, 2024, 41 (4): 1052-1057. )

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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