Treffer: Coniferest: a complete active anomaly detection framework
Title:
Coniferest: a complete active anomaly detection framework
Authors:
Contributors:
Laboratoire de Physique de Clermont (LPC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA), SNAD team
Source:
Astron.Comput. ; https://hal.science/hal-04751192 ; Astron.Comput., 2025, 52, pp.100960. ⟨10.1016/j.ascom.2025.100960⟩
Publisher Information:
CCSD
Publication Year:
2025
Collection:
HAL Clermont Auvergne (Université Blaise Pascal Clermont-Ferrand / Université d'Auvergne)
Subject Terms:
Document Type:
Fachzeitschrift
article in journal/newspaper
Language:
English
Relation:
info:eu-repo/semantics/altIdentifier/arxiv/2410.17142; ARXIV: 2410.17142; INSPIRE: 2842291
DOI:
10.1016/j.ascom.2025.100960
Accession Number:
edsbas.63447618
Database:
BASE
Weitere Informationen
International audience ; We present coniferest, an open source generic purpose active anomaly detection framework written in Python. The package design and implemented algorithms are described. Currently, static outlier detection analysis is supported via the Isolation forest algorithm. Moreover, Active Anomaly Discovery (AAD) and Pineforest algorithms are available to tackle active anomaly detection problems. The algorithms and package performance are evaluated on a series of synthetic datasets. We also describe a few success cases which resulted from applying the package to real astronomical data in active anomaly detection tasks within the SNAD project.