Abstract
The technological advances in the industrial sector have emphasized the importance of anomaly detection, which represents a critical task to a achieve a systems optimization. In this context, many different outlier detection techniques have been developed. The boundary methods have presented successfully results in many one-class problems. Specifically convex hull approximations have offered good performance. However, this approach leads to misclassification when it is applied to non-convex sets. This paper proposes a hybrid one-class topology based on an approximate convex hull approach to solve the problem of anomaly detection over non-convex sets. The proposal is assessed and validated with successful results.
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Jove, E. et al. (2020). A Hybrid One-Class Topology for Non-convex Sets. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_28
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