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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 9
2621-2625

Text classification combining text graph convolution and ensemble learning

Zhou Xuanlang
Qiu Weigen
Zhang Lichen
School of Computer Science & Technology, Guangdong University of Technology, Guangzhou 510006, China
DOI: 10.19734/j.issn.1001-3695.2022.03.0066

Abstract

In order to improve the accuracy of text classification and solve the problem of insufficient utilization of node features by text graph convolution neural network, this paper proposed a new text classification model, which integrated the advantages of text graph convolution and Stacking integrated learning method. The model firstly learned the global expression of documents and words and the grammatical structure information of documents through text graph convolution neural network, and then secondary learned the features extracted by text graph convolution through integrated learning, so as to make up for the insufficient utilization of text graph convolution node features, and improved the accuracy of single label text classification and the generalization ability of the whole model. In order to reduce the time consumption of ensemble learning, the fusion algorithm removed the k-fold cross verification mechanism in ensemble learning. The fusion algorithm realized the correlation between text graph convolution and Stacking integrated learning method. The classification effect on R8, R52, MR, Ohsumed, 20NG and other datasets is improved by more than 1.5%, 2.5%, 11%, 12% and 7% respectively compared with the traditional classification model. This method performs well in the comparison of classification algorithms in the same field.

Foundation Support

国家自然科学基金资助项目(61873068)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.03.0066
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 9
Section: Algorithm Research & Explore
Pages: 2621-2625
Serial Number: 1001-3695(2022)09-009-2621-05

Publish History

[2022-04-29] Accepted Paper
[2022-09-05] Printed Article

Cite This Article

周玄郎, 邱卫根, 张立臣. 融合文本图卷积和集成学习的文本分类方法 [J]. 计算机应用研究, 2022, 39 (9): 2621-2625. (Zhou Xuanlang, Qiu Weigen, Zhang Lichen. Text classification combining text graph convolution and ensemble learning [J]. Application Research of Computers, 2022, 39 (9): 2621-2625. )

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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