Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 2
414-420

Multi-scale classification algorithm

Zhang Lulua,b,c
Zhao Shulianga,b,c
Tian Zhenzhena,b,c
Chen Runzid
a. College of Computer & Cyber Security, b. Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security, c. Key Laboratory of Network & Information Security, d. School of Mathematical Sciences, Hebei Normal University, Shijiazhuang 050024, China
DOI: 10.19734/j.issn.1001-3695.2020.01.0007

Abstract

Multi-scale classification mining are mostly limited to spatial data, and there are few researches on scale characte-ristics of general data. By solving the above problems, this paper tried to study the universal multi-scale classification method, in order to expand the scope of multi-scale application. From the perspective of spatial data estimation, combined the hierarchical theory and scale characteristics, and based on the discretization method of probability density estimation, this paper studied the classification mining on multi-scale characteristics of general data. Based on the theory of non-local mean and double cube interpolation, using Q statistics and inconsistent measurement to operate, it proposed the upscaling algorithm of multi-scale classification and downscaling algorithm of multi-scale classification. This paper performed experiments on UCI data sets and H province real population data set, and compared with CFW, MSCSUA, MSCSDA and other algorithms. The results show that the algorithms in this paper are feasible and effective. Compared with other algorithms, the upscaling algorithm improves accuracy by 4.5%, F-score by 4.8 % and NMI by 12.3% and the downscaling algorithm improves the correspon-ding indexes by 5.3%, 6.6% and 11.8%.

Foundation Support

国家社科基金重大项目(13&ZD091,18ZDA200)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.01.0007
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 2
Section: Algorithm Research & Explore
Pages: 414-420
Serial Number: 1001-3695(2021)02-016-0414-07

Publish History

[2021-02-05] Printed Article

Cite This Article

张璐璐, 赵书良, 田真真, 等. 多尺度分类挖掘算法 [J]. 计算机应用研究, 2021, 38 (2): 414-420. (Zhang Lulu, Zhao Shuliang, Tian Zhenzhen, et al. Multi-scale classification algorithm [J]. Application Research of Computers, 2021, 38 (2): 414-420. )

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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