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Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm

Luis Martí 1, 2, 3 Arsene Fansi-Tchango 4 Laurent Navarro 4 Marc Schoenauer 1, 2 
1 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : 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.
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Submitted on : Wednesday, October 26, 2016 - 6:16:20 PM
Last modification on : Tuesday, October 25, 2022 - 4:16:58 PM


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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⟩



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