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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 9
2664-2669

Addressing long-tail problem in multi-label text classification based on mutual information

Pan Lihu1
Li Xiaohua1
Zhang Rui1
Xie Binhong1
Yang Nan1
Zhang Linliang2
1. College of Computer Science & Technology, Taiyuan University of Science & Technology, Taiyuan 030024, China
2. Institute of Information Technology, Shanxi Institute of Transportation Science, Taiyuan 030006, China
DOI: 10.19734/j.issn.1001-3695.2023.12.0623

Abstract

To address the long-tail problem in MLTC where current methods often compromise the original data distribution, resulting in reduced generalization performance on real data and ineffective mitigation of the long-tail distribution issue, this paper proposed a method of multi-label text classification with long-tail distribution(MLTC-LD). Initially, it created a relationship matrix for label samples to compute dependencies between label samples. Then, considering the degree of relationships between label samples, it constructed a neighbor selector, which used information from neighbors with strong relationships as the main semantic features and as prior information. Finally, by incorporating this prior information through a graph attention neural network into the classifier, the method aimed to enrich the knowledge of categories with abundant head data to improve the performance of categories with sparse tail data. An extensive comparative analysis of MLTC-LD with eight baseline models across three different datasets was conducted. The experimental results show that MLTC-LD improves precision by 3.5%, 0.3%, and 1.5% respectively, compared to the best-performing HGLRN, demonstrating the effectiveness of this approach.

Foundation Support

山西省自然科学基金资助项目(201901D111258)
山西省智能软件与人机环境系统研究生联合培养示范基地项目(2022JD11)
山西省留学人员管理委员会资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.12.0623
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 9
Section: Algorithm Research & Explore
Pages: 2664-2669
Serial Number: 1001-3695(2024)09-014-2664-06

Publish History

[2024-03-19] Accepted Paper
[2024-09-05] Printed Article

Cite This Article

潘理虎, 李小华, 张睿, 等. 基于互信息解决多标签文本分类中的长尾问题 [J]. 计算机应用研究, 2024, 41 (9): 2664-2669. (Pan Lihu, Li Xiaohua, Zhang Rui, et al. Addressing long-tail problem in multi-label text classification based on mutual information [J]. Application Research of Computers, 2024, 41 (9): 2664-2669. )

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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