NDLI: Research of Neural Network Structural Optimization Based on Information Entropy

Content Provider	IET Digital Library
Author	G, Danyang W A N O, Fangming S H A
Abstract	In the application of deep learning, the depth and width of the neural network structure have a great influence on the learning performance of the neural network. This paper focuses on structural optimization of depth and width, leveraging the information entropy model and decision tree strategy as feature selection and structural adjustment to optimize neural network candidates. Therefore, a decision tree-based heuristic optimization algorithm for neural network structural adjustment is proposed. Furthermore, the proposed approach is applied to fully-connected neural networks trained on the Iris dataset, and the proposed approach is verified effective via experimental simulation.
Starting Page	632
Ending Page	638
Page Count	7
ISSN	10224653
Volume Number	29
e-ISSN	20755597
Issue Number	Issue 4, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/29/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2020.05.006
Journal	Chinese Journal of Electronics
Publisher Date	2020-07-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	Combinatorial Mathematics Decision Tree Decision Tree Strategy Decision Tree-based Heuristic Optimization Algorithm Deep Learning Entropy Information Entropy Model Knowledge Engineering Technique Learning in AI Learning Performance Neural Computing Technique Neural Nets Neural Network Candidates Neural Network Structural Adjustment Neural Network Structural Optimization Neural Network Structure Optimisation Optimisation Technique Statistics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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