The embedded feature selection method using ANT colony optimization with structured sparsity norms

Feature selection is important in many machine learning applications. Our results demonstrate that it is not necessary to use all features of a dataset to perform classification and achieve lower classification error, which leads to higher accuracy. By selecting meaningful features and reducing the...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Computing 2025, Vol.107 (1), p.29, Article 29
Hauptverfasser:	Nemati, Khadijeh, Sheikhani, Amir Hosein Refahi, Kordrostami, Sohrab, Roudposhti, Kamrad Khoshhal
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Ant colony optimization Artificial Intelligence Artificial neural networks Classification Computer Appl. in Administrative Data Processing Computer Communication Networks Computer Science Datasets Feature selection Information Systems Applications (incl.Internet) Machine learning Regular Paper Software Engineering Sparsity
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!