Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 7
2079-2086

Multi-label feature selection method with enhanced learning of label correlations

Teng Shaohua1
Lu Jianlei1
Teng Luyao2
Zhang Wei1
1. School of Computer, Guangdong University of Technology, Guangzhou 510006, China
2. School of Information Engineering, Guangzhou Panyu Polytechnic, Guangzhou 511483, China
DOI: 10.19734/j.issn.1001-3695.2023.11.0550

Abstract

Aiming at two problems of existing multi-label feature selection methods: first, ignoring the influence of noise information in the process of learning label correlations; second, neglecting to explore the comprehensive label information of each cluster, the paper proposed a multi-label feature selection method that enhanced label correlation learning. Initially, it clustered the samples and treated each cluster center as a representative instance of the comprehensive semantic information of the samples, while computing its corresponding label vectors which reflected the importance of different labels contained in each cluster. Then, through the label-level self-representation of the original samples and the center of each cluster, it both captured the label correlations in the original label space, and explored the label correlations within each cluster. Finally, the self-representation coefficient matrix was sparse to reduce the effect of noise, and the original sample and the representative instance of each cluster were mapped from the feature space to the reconstructed label space for feature selection. Experimental results on nine multi-labeled datasets show that the proposed algorithm has better performance compared with other methods.

Foundation Support

国家自然科学基金资助项目(6197210)
广州市科技计划资助项目(2023A04J1729)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.11.0550
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 7
Section: Algorithm Research & Explore
Pages: 2079-2086
Serial Number: 1001-3695(2024)07-022-2079-08

Publish History

[2024-01-25] Accepted Paper
[2024-07-05] Printed Article

Cite This Article

滕少华, 卢建磊, 滕璐瑶, 等. 增强学习标签相关性的多标签特征选择方法 [J]. 计算机应用研究, 2024, 41 (7): 2079-2086. (Teng Shaohua, Lu Jianlei, Teng Luyao, et al. Multi-label feature selection method with enhanced learning of label correlations [J]. Application Research of Computers, 2024, 41 (7): 2079-2086. )

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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