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 performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit.

• 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