Coniferest: A complete active anomaly detection framework

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.

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Publication Details

Journal
Astronomy and Computing
Published
2025-04-04
DOI
https://doi.org/10.1016/j.ascom.2025.100960
Citations
6
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
14.89

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article

Coniferest: A complete active anomaly detection framework

T. A. Semenikhin, A.D. Lavrukhina, M. Kornilov, É. Gangler et al.
6 citations
Astronomy and Computing
Anomaly Detection Techniques and Applications
14.89
article

Coniferest: A complete active anomaly detection framework

T. A. Semenikhin, A.D. Lavrukhina, M. Kornilov, É. Gangler, Sreevarsha Sreejith, Konstantin Malanchev, В. С. Королев, Émille E. O. Ishida, M. V. Pruzhinskaya, Etienne Russeil, A. Volnova
article en
6 citations

Abstract

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.

Astronomy and ComputingVol. 52
National Research University Higher School of Economics (RU), Centre National de la Recherche Scientifique (FR), University of Illinois Urbana-Champaign (US), Lomonosov Moscow State University (RU), Space Research Institute (RU), University of Surrey (GB), University of Clermont Auvergne (FR), Clermont Université (FR), Carnegie Mellon University (US)
Russian Science Foundation
Openalex Percentile: Top 1%
Anomaly Detection Techniques and Applications
14.89
DOIOpen Access PDFOpenAlex
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