Research on Feature Selection Method of Group Lasso Hypergraph Regularization and Depression Classification
Purposes In the research of depression classification and diagnosis, feature selection plays a crucial role. Methods To address the issues of missing group effect information in existing hypergraph regularized feature selection methods, the group lasso-based hypergraph regularized feature selection...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Editorial Office of Journal of Taiyuan University of Technology
2023-09-01
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| Series: | Taiyuan Ligong Daxue xuebao |
| Subjects: | |
| Online Access: | https://tyutjournal.tyut.edu.cn/englishpaper/show-2115.html |
| _version_ | 1797208295351517184 |
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| author | Dongxi GUO Yao LI Junjie CHEN |
| author_facet | Dongxi GUO Yao LI Junjie CHEN |
| author_sort | Dongxi GUO |
| collection | DOAJ |
| description | Purposes In the research of depression classification and diagnosis, feature selection plays a crucial role. Methods To address the issues of missing group effect information in existing hypergraph regularized feature selection methods, the group lasso-based hypergraph regularized feature selection approach is proposed. Specifically, the functional magnetic resonance imaging (fMRI) dataset is preprocessed first for depression. Second, on the basis of the preprocessed fMRI data, five brain network models under different scales are constructed and the topological attributes are calculated to extract features. After feature extracting, the group lasso method is introduced to build hypergraph and the hypergraph regularized feature selection method is employed to select features. At last, classification model is constructed by using support vector machine (SVM) and its performance is evaluated. Additionally, the effectiveness of the proposed method is validated on UCI datasets. Findings The demonstrate that the proposed method outperforms traditional feature selection methods across five different node templates. Moreover, for similar numbers of nodes in different templates, superior classification diagnostic performance is achieved. |
| first_indexed | 2024-04-24T09:36:32Z |
| format | Article |
| id | doaj.art-79f973217daf4a17bc67b5f6e7e6511e |
| institution | Directory Open Access Journal |
| issn | 1007-9432 |
| language | English |
| last_indexed | 2024-04-24T09:36:32Z |
| publishDate | 2023-09-01 |
| publisher | Editorial Office of Journal of Taiyuan University of Technology |
| record_format | Article |
| series | Taiyuan Ligong Daxue xuebao |
| spelling | doaj.art-79f973217daf4a17bc67b5f6e7e6511e2024-04-15T09:17:01ZengEditorial Office of Journal of Taiyuan University of TechnologyTaiyuan Ligong Daxue xuebao1007-94322023-09-0154583884510.16355/j.tyut.1007-9432.2023.05.0111007-9432(2023)05-0838-08Research on Feature Selection Method of Group Lasso Hypergraph Regularization and Depression ClassificationDongxi GUO0Yao LI1Junjie CHEN2College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, ChinaCollege of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, ChinaCollege of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, ChinaPurposes In the research of depression classification and diagnosis, feature selection plays a crucial role. Methods To address the issues of missing group effect information in existing hypergraph regularized feature selection methods, the group lasso-based hypergraph regularized feature selection approach is proposed. Specifically, the functional magnetic resonance imaging (fMRI) dataset is preprocessed first for depression. Second, on the basis of the preprocessed fMRI data, five brain network models under different scales are constructed and the topological attributes are calculated to extract features. After feature extracting, the group lasso method is introduced to build hypergraph and the hypergraph regularized feature selection method is employed to select features. At last, classification model is constructed by using support vector machine (SVM) and its performance is evaluated. Additionally, the effectiveness of the proposed method is validated on UCI datasets. Findings The demonstrate that the proposed method outperforms traditional feature selection methods across five different node templates. Moreover, for similar numbers of nodes in different templates, superior classification diagnostic performance is achieved.https://tyutjournal.tyut.edu.cn/englishpaper/show-2115.htmlhypergraphfeature selectiongroup lassosparseclassificationdepression |
| spellingShingle | Dongxi GUO Yao LI Junjie CHEN Research on Feature Selection Method of Group Lasso Hypergraph Regularization and Depression Classification Taiyuan Ligong Daxue xuebao hypergraph feature selection group lasso sparse classification depression |
| title | Research on Feature Selection Method of Group Lasso Hypergraph Regularization and Depression Classification |
| title_full | Research on Feature Selection Method of Group Lasso Hypergraph Regularization and Depression Classification |
| title_fullStr | Research on Feature Selection Method of Group Lasso Hypergraph Regularization and Depression Classification |
| title_full_unstemmed | Research on Feature Selection Method of Group Lasso Hypergraph Regularization and Depression Classification |
| title_short | Research on Feature Selection Method of Group Lasso Hypergraph Regularization and Depression Classification |
| title_sort | research on feature selection method of group lasso hypergraph regularization and depression classification |
| topic | hypergraph feature selection group lasso sparse classification depression |
| url | https://tyutjournal.tyut.edu.cn/englishpaper/show-2115.html |
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