{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:02:55Z","timestamp":1760144575539,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T00:00:00Z","timestamp":1714608000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>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\u2019s 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\u2019s superiority in terms of both efficiency and accuracy.<\/jats:p>","DOI":"10.3390\/sym16050549","type":"journal-article","created":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T07:04:14Z","timestamp":1714633454000},"page":"549","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Feature-Selection Method Based on Graph Symmetry Structure in Complex Networks"],"prefix":"10.3390","volume":"16","author":[{"given":"Wangchuanzi","family":"Deng","sequence":"first","affiliation":[{"name":"Air Traffic Control and Navigation College, Air Force Engineering University, Xi\u2019an 710051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minggong","family":"Wu","sequence":"additional","affiliation":[{"name":"Air Traffic Control and Navigation College, Air Force Engineering University, Xi\u2019an 710051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3782-0785","authenticated-orcid":false,"given":"Xiangxi","family":"Wen","sequence":"additional","affiliation":[{"name":"Air Traffic Control and Navigation College, Air Force Engineering University, Xi\u2019an 710051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0167-2004","authenticated-orcid":false,"given":"Yuming","family":"Heng","sequence":"additional","affiliation":[{"name":"Unit of 95129, Kaifeng 475000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"You","sequence":"additional","affiliation":[{"name":"Equipment Management and Unmanned Aerial Vehicle Engineering College, Air Force Engineering University, Xi\u2019an 710051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.dss.2019.03.011","article-title":"A multi-objective approach for profit-driven feature selection in credit scoring","volume":"120","author":"Kozodoi","year":"2019","journal-title":"Decis. 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