Répertoire de publications
de recherche en accès libre
de recherche en accès libre
Henni, Khadidja; Mezghani, Neila et Gouin-Vallerand, Charles (2018). Unsupervised graph-based feature selection via subspace and pagerank centrality. Expert Systems with Applications, 114, 46-53. doi: 10.1016/j.eswa.2018.07.029
Fichier(s) associé(s) à ce document :|
PDF
- eswa-revision.pdf
Contenu du fichier : Manuscrit accepté (révisé après évaluation) |
|
| Catégorie de document : | Articles de revues |
|---|---|
| Évaluation par un comité de lecture : | Oui |
| Étape de publication : | Publié |
| Résumé : | Feature selection has become an indispensable part of intelligent systems, especially with the proliferation of high dimensional data. It identifies the subset of discriminative features leading to better learning performances, i.e., higher learning accuracy, lower computational cost and significant model interpretability. This paper proposes a new efficient unsupervised feature selection method based on graph centrality and subspace learning called UGFS for ‘Unsupervised Graph-based Feature Selection’. The method maps features on an affinity graph where the relationships (edges) between feature nodes are defined by means of data points subspace preference. Feature importance score is then computed on the entire graph using a centrality measure. For this purpose, we investigated the Google’s PageRank method originally introduced to rank web-pages. The proposed feature selection method has been evaluated using classification and redundancy rates measured on the selected feature subsets. Comparisons with the well-known unsupervised feature selection methods, on gene/expression benchmark datasets, demonstrate the validity and the efficiency of the proposed method. |
| Adresse de la version officielle : | https://www.sciencedirect.com/science/article/pii/... |
| Déposant: | Mezghani, Neila |
| Responsable : | Neila Mezghani |
| Dépôt : | 22 oct. 2018 18:44 |
| Dernière modification : | 09 mars 2020 13:53 |
|
RÉVISER |
