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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 4
1037-1042

Multi-label classification with structural property preserving and correlation learning

Zhang Qiliang
Lou Hengrui
Ju Dianchun
School of Electrical & Information Engineering, Jiangsu University of Science & Technology, Zhangjiagang Jiangsu 215600, China
DOI: 10.19734/j.issn.1001-3695.2021.09.0398

Abstract

Most of the existing multi-label learning techniques consider the correlation learning problem but ignore the inconsistency feature of structural properties, which can change the structural property of the label space obtained by function mapping and it can directly affect the classification performance. In order to deal with this problem, this paper proposed a multi-label classification algorithm based on structural property preserving policy and correlation learning policies. Firstly, the algorithm constructed a mapping function to realize the mapping between the feature space and the label space. Secondly, it used structural property preserving strategy based on feature data to reduce the difference between feature data caused by linear transformation. Finally, this paper introduced the correlation learning strategy based on label data to further optimize the parameters of the proposed algorithm to improve the classification performance. The experimental results through a group of benchmark instances show that compared with the other classical multi-label classification algorithm, the proposed algorithm is much superior in classification performance, and the algorithm has great effectiveness.

Foundation Support

国家“十三五”重点研发计划资助项目(2017YFB0603801)
国家自然科学基金青年项目(62106145)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.09.0398
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 4
Section: Algorithm Research & Explore
Pages: 1037-1042
Serial Number: 1001-3695(2022)04-013-1037-06

Publish History

[2021-11-28] Accepted Paper
[2022-04-05] Printed Article

Cite This Article

张其亮, 娄恒瑞, 居殿春. 基于结构性质保持和相关性学习的多标记分类算法 [J]. 计算机应用研究, 2022, 39 (4): 1037-1042. (Zhang Qiliang, Lou Hengrui, Ju Dianchun. Multi-label classification with structural property preserving and correlation learning [J]. Application Research of Computers, 2022, 39 (4): 1037-1042. )

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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