Algorithm Research & Explore
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1072-1074

Self-training semi-supervised classification based on density of data

Ai Zhenpeng
Wang Zhenyou
College of Applied Mathematics, Guangdong University of Technology, Guangzhou 510520, China

Abstract

It is a common problem in many practical applications that unlabeled samples is sufficient but labeled ones is very rare. A successful method to tackle this problem is self-training semi-supervised classification. This paper introduced a self-training semi-supervised classification method, in which entire data was divided into three parts based on density of data, so that the real structure of data space could be found. And then, it proposed a framework for self-training semi-supervised classification, in which the structure of data space was integrated into the self-training iterative process to help train a better classifier. Experiments on 6 data sets from UCI show that the classifier gets from the proposed method has a better performance than the ones gets from supervised method with few labeled samples and standard self-training semi-supervised classification method.

Foundation Support

广州市科技计划资助项目(201707010435)
广东省研究生教育创新改革项目(2014JGXM-MS17)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.12.0753
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 4
Section: Algorithm Research & Explore
Pages: 1072-1074
Serial Number: 1001-3695(2019)04-025-1072-03

Publish History

[2019-04-05] Printed Article

Cite This Article

艾震鹏, 王振友. 基于数据密度的半监督自训练分类算法 [J]. 计算机应用研究, 2019, 36 (4): 1072-1074. (Ai Zhenpeng, Wang Zhenyou. Self-training semi-supervised classification based on density of data [J]. Application Research of Computers, 2019, 36 (4): 1072-1074. )

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.

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