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 multiobjective 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.
CITATION STYLE
Martí, L., Fansi-Tchango, A., Navarro, L., & Schoenauer, M. (2016). Anomaly detection with the Voronoi diagram evolutionary algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9921 LNCS, pp. 697–706). Springer Verlag. https://doi.org/10.1007/978-3-319-45823-6_65
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