Feature selection by multi-objective optimisation: application to network anomaly detection by hierarchical self-organising maps
Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performanc...
- Autores:
-
De-La-Hoz-Franco, Emiro
De la Hoz Correa, Eduardo Miguel
Ortiz, Andrés
Ortega, Julio
Martínez Álvarez, Antonio
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2014
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/4197
- Acceso en línea:
- https://hdl.handle.net/11323/4197
https://repositorio.cuc.edu.co/
- Palabra clave:
- Feature selection
Growing self-srganising maps
IDS
Multi-objective optimization
Network anomaly detection
Unsupervised clustering
Selección de características
Crecientes mapas auto-organizados.
IDS
Optimización multiobjetivo
Detección de anomalías de red
Agrupamiento sin supervisión
- Rights
- openAccess
- License
- Attribution-NonCommercial-ShareAlike 4.0 International