Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 3
833-838

Research on cost-sensitive multi-granularity three-way decision model for deep belief network

Lyu Yanna1
Gou Guanglei1
Zhang Libo2
Zhang Yaohong1
1. College of Computer Science & Engineering, Chongqing University of Technology, Chongqing 400054, China
2. School of Artificial Intelligence, Southwest University, Chongqing 400715, China
DOI: 10.19734/j.issn.1001-3695.2022.08.0406

Abstract

Optimal granularity selection is a critical link in constructing multi-granularity features in autoencoder networks. Aiming at the problem of unreasonable granularity selection method in autoencoder networks, which leds to unsatisfactory feature extraction effect, high misclassification cost, and high testing cost, this paper proposed a granularity layer selection strategy based on mini-batch gradient descent(MBGD). This approach reconstructed the multi-granularity space by changing the granularity selection method and designed a new cost-sensitive multi-granularity three-way decision model based on a deep belief network(DBN). The superior granularity selection method enhanced the feature extraction ability of the network, promoted the construction of multi-granularity space to develop towards the fastest reaching the most fine-grained area, and reduced the image reconstruction error to achieve more minor misclassification cost and test cost. The experimental results show that providing a reasonable granularity selection strategy improves the decision accuracy of the cost-sensitive multi-granularity three-way decision model, and can obtain the optimal granular layer with the smallest total cost faster under the given cost.

Foundation Support

国家自然科学基金资助项目(62141201,62106205)
重庆市自然科学基金资助项目(cstc2021jcyj-msxmX0824)
重庆理工大学研究生教育高质量发展行动计划资助成果(gzlcx20222059,gzlcx20223188)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.08.0406
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 3
Section: Algorithm Research & Explore
Pages: 833-838
Serial Number: 1001-3695(2023)03-031-0833-06

Publish History

[2022-10-27] Accepted Paper
[2023-03-05] Printed Article

Cite This Article

吕艳娜, 苟光磊, 张里博, 等. 深度置信网络的代价敏感多粒度三支决策模型研究 [J]. 计算机应用研究, 2023, 40 (3): 833-838. (Lyu Yanna, Gou Guanglei, Zhang Libo, et al. Research on cost-sensitive multi-granularity three-way decision model for deep belief network [J]. Application Research of Computers, 2023, 40 (3): 833-838. )

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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