Fuzzy-Vocabulary-Based Detection and Explanation of Anomalies

Rahul Nath (1) , Grégory Smits (2, 3, 4) , Olivier Pivert (5)

1 UiB - University of Bergen
2 IMT Atlantique - INFO - Département Informatique
3 Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
4 Lab-STICC_MOTEL - Equipe MOTEL
5 IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires

Fuzzy partitions associated with linguistic variables are particularly useful to provide users with a description of the data. However, designing a fuzzy vocabulary that makes it possible to linguistically describe the data distribution and its inner structure is a tedious task. This paper introduces a novel strategy to infer possible fuzzy partitions from the data distribution with the objective to have available modalities to describe both dense and sparse regions. A data inner structure as well as the anomalies are then identified using this vocabulary whose terms are also used to provide users with contrastive explanations about the found anomalies.

Keywords: Fuzzy vocabulary inference; linguistic summaries; anomaly detection; anomaly explanation

Domains: Artificial Intelligence [cs.AI]

Rahul Nath, Grégory Smits, Olivier Pivert. Fuzzy-Vocabulary-Based Detection and Explanation of Anomalies. FUZZ 2023: IEEE International Conference on Fuzzy Systems, Aug 2023, Incheon, South Korea. ⟨10.1109/FUZZ52849.2023.10309771⟩. ⟨hal-04122596⟩