Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 3
744-750

Construction and classification research of approximately unbiased sparse brain functional hypernetwork model based on group selection

Li Yaoa
Zhou Zihaob
Liang Jiaruic
Ibegbu Nnamdi Juliana
Guo Haoa
Chen Junjiea
a. College of Information & Computer, b. College of Mathematics, c. College of Software, Taiyuan University of Science & Technology, Jinzhong Shanxi 030600, China
DOI: 10.19734/j.issn.1001-3695.2021.08.0363

Abstract

Aiming at the problem of the lack of group effecting explanation ability and network bias in the brain functional hyper-network model based on the LASSO method, this paper proposed two approximate unbiased sparse brain function hypernetwork models based on group variable selection to improve the construction of hypernetworks, such as the group minimax con-cave penalty(gMCP) method and the group smoothly clipped absolute deviation method(gSCAD). Then it applied them to the classification research of depression. Classification results show that the classification performance of the two proposed methods are better than the traditional hyper network model, and the classification accuracy of gMCP is the highest, reaching 86.36%. These results indicate that in order to build an effective brain functional hypernetwork model, not only the group effecting should be considered, the bias in the selection of model variables should also be considered. Moreover, taking into account the bias of the hypernetwork, it can more accurately represent the complex and high-order multivariate interactive information of the human brain that choose a more relaxed punishment method for the selection the target variable.

Foundation Support

国家自然基金资助项目(61672374,61876124,61472270,61741212,61976150,61873178)
山西省重点研发计划项目(201803D31043)
山西省科技厅应用基础研究项目青年面上项目(201801D121135,201803D31043)
山西省研究生教育创新项目(2020BY131)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.08.0363
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 3
Section: Algorithm Research & Explore
Pages: 744-750
Serial Number: 1001-3695(2022)03-017-0744-07

Publish History

[2021-11-29] Accepted Paper
[2022-03-05] Printed Article

Cite This Article

李瑶, 周子淏, 梁家瑞, 等. 基于组选择的近似无偏稀疏脑功能超网络模型构建与分类研究 [J]. 计算机应用研究, 2022, 39 (3): 744-750. (Li Yao, Zhou Zihao, Liang Jiarui, et al. Construction and classification research of approximately unbiased sparse brain functional hypernetwork model based on group selection [J]. Application Research of Computers, 2022, 39 (3): 744-750. )

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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