Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2016

Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm

Résumé

This paper presents the Voronoi diagram-based evolutionary algorithm (VorEAl). VorEAl partitions input space in abnormal/normal subsets using Voronoi diagrams. Diagrams are evolved using a multi-objective bio-inspired approach in order to conjointly optimize classification metrics while also being able to represent areas of the data space that are not present in the training dataset. As part of the paper VorEAl is experimentally validated and contrasted with similar approaches.
Fichier principal
Vignette du fichier
main.pdf (645.36 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-01387621 , version 1 (26-10-2016)

Identifiants

Citer

Luis Martí, Arsene Fansi-Tchango, Laurent Navarro, Marc Schoenauer. Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm. Parallel Problem Solving from Nature – PPSN XIV, Sep 2016, Edinburgh, United Kingdom. pp.697-706, ⟨10.1007/978-3-319-45823-6_65⟩. ⟨hal-01387621⟩
160 Consultations
194 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More