Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 7
1970-1974

Scaling-down algorithm of multi-scale classification based on generalized fractal interpolation theory

Li Jiaxinga,b,c
Zhao Shulianga,b,c
An Leia,b,c
Li Changjinga,b,c
a. College of Mathematics & Information Science, b. Hebei Key Laboratory of Computational Mathematics & Applications, c. Institute of Mobile Internet of Things, Hebei Normal University, Shijiazhuang 050024, China
DOI: 10.19734/j.issn.1001-3695.2018.01.0031

Abstract

The research of multi-scale data mining mainly applies to space remote sensing image data sets, and conducts scale division based on the resolution or regional segmentation of the images, then analysis knowledge on each scale layer. Recently, there are quite a few learners apply the multi-scale data mining to general data sets, and conduct scale division based on the level theory, concept hierarchy and inclusion degree etc., study the distribution rule on different scale layers, and then found significant facts, for example, multi-scale association rules, multi-scale clustering. But it has not been involved in the field of the classification mining. This paper defined the concept of generalized fractal interpolation theory, broke the situation that limited to the use of the IFS, and extended the application of the fractal interpolation. Then, it proposed a multi-scale classification scaling-down algorithm based on the generalized fractal interpolation theory named MSCSDA. This paper performed experiments on four UCI benchmark data sets, and one real data set(H province part of the population). Then it analyzed the experimental results compare MSCSDA with KNN, decision tree and LIBSVM algorithms on different data sets. The experimental results show that the MSCSDA gives better results in terms of classification than the others.

Foundation Support

国家自然科学基金资助项目(71271067)
国家社科基金重大项目(13&ZD091)
河北省高等学校科学技术研究项目(QN2014196)
河北师范大学硕士基金资助项目(xj2015003)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.01.0031
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 7
Section: Algorithm Research & Explore
Pages: 1970-1974
Serial Number: 1001-3695(2019)07-011-1970-05

Publish History

[2019-07-05] Printed Article

Cite This Article

李佳星, 赵书良, 安磊, 等. 基于广义分形插值理论的多尺度分类尺度下推算法 [J]. 计算机应用研究, 2019, 36 (7): 1970-1974. (Li Jiaxing, Zhao Shuliang, An Lei, et al. Scaling-down algorithm of multi-scale classification based on generalized fractal interpolation theory [J]. Application Research of Computers, 2019, 36 (7): 1970-1974. )

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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