Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm
This addresses anomaly detection, a domain-specific problem, with an incremental approach.
The paper tackles anomaly detection by introducing VorEAl, a method that partitions input space into abnormal/normal subsets using Voronoi diagrams evolved with a multi-objective bio-inspired approach, and it is experimentally validated against similar approaches.
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.