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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 6
1683-1688

Segmentation training data selection method based on K-means clustering

Zhou Yu
Sun Hongyu
Zhu Wenhao
Ren Qinchai
School of Electric Power, North China University of Water Resources & Electric Power, Zhengzhou 450045, China
DOI: 10.19734/j.issn.1001-3695.2020.09.0236

Abstract

In order to improve the performance of neural network classifier, this paper proposed a segmented sample data selection method based on K-means clustering. First, K-means clustering was used to cluster the training samples according to the number of known categories, and then it compared the samples before and after clustering to find out the sample sets with clustering errors and the correct sample sets. Second, this method sorted the correct sample set of clustering and divided it into five segments according to the distance from each sample to the clustering center and selected the samples of odd segments and the samples of clustering errors to form a new training sample set. The proposed method could select informative data, delete redundant data, reduce the number of training data set and improve the quality of training data set. This method used some classifiers based on neural network combing the artificial data set and UCI data sets to study. Experimental results show that the performance of the three neural network classifiers is improved while the average compression ratio of the training samples is 66.93%.

Foundation Support

河南省高等学校青年骨干教师培养计划资助项目(2018GGJS079)
国家自然科学基金资助项目(U1504622,31671580)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.09.0236
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 6
Section: Algorithm Research & Explore
Pages: 1683-1688
Serial Number: 1001-3695(2021)06-015-1683-06

Publish History

[2021-06-05] Printed Article

Cite This Article

周玉, 孙红玉, 朱文豪, 等. 基于K均值聚类的分段样本数据选择方法 [J]. 计算机应用研究, 2021, 38 (6): 1683-1688. (Zhou Yu, Sun Hongyu, Zhu Wenhao, et al. Segmentation training data selection method based on K-means clustering [J]. Application Research of Computers, 2021, 38 (6): 1683-1688. )

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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