A Feature-Selection Method Based on Graph Symmetry Structure in Complex Networks

This study aims to address the issue of redundancy and interference in data-collection systems by proposing a novel feature-selection method based on maximum information coefficient (MIC) and graph symmetry structure in complex-network theory. The method involves establishing a weighted feature netw...

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Bibliographic Details
Published in:Symmetry (Basel) 2024-05, Vol.16 (5), p.549
Main Authors: Deng, Wangchuanzi, Wu, Minggong, Wen, Xiangxi, Heng, Yuming, You, Liang
Format: Article
Language:English
Subjects:
Algorithms
Classification
Datasets
Feature selection
Methods
Random variables
Redundancy
Support vector machines
Symmetry
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Summary:This study aims to address the issue of redundancy and interference in data-collection systems by proposing a novel feature-selection method based on maximum information coefficient (MIC) and graph symmetry structure in complex-network theory. The method involves establishing a weighted feature network, identifying key features using dominance set and node strength, and employing the binary particle-swarm algorithm and LS-SVM algorithm for solving and validation. The model is implemented on the UNSW-NB15 and UCI datasets, demonstrating noteworthy results. In comparison to the prediction methods within the datasets, the model’s running speed is significantly reduced, decreasing from 29.8 s to 6.3 s. Furthermore, when benchmarked against state-of-the-art feature-selection algorithms, the model achieves an impressive average accuracy of 90.3%, with an average time consumption of 6.3 s. These outcomes highlight the model’s superiority in terms of both efficiency and accuracy.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym16050549