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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Acknowledgements
This work has been funded by the project PIA-FSN-P3344-146479. Authors wish to thank the reviewers for their fruitful comments.
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Martí, L., Fansi-Tchango, A., Navarro, L., Schoenauer, M. (2016). Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm. In: Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science(), vol 9921. Springer, Cham. https://doi.org/10.1007/978-3-319-45823-6_65
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DOI: https://doi.org/10.1007/978-3-319-45823-6_65
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