Coniferest: a complete active anomaly detection framework
Instrumentation and Methods for Astrophysics
2024-11-18 v2 Human-Computer Interaction
Machine Learning
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.
Cite
@article{arxiv.2410.17142,
title = {Coniferest: a complete active anomaly detection framework},
author = {M. V. Kornilov and V. S. Korolev and K. L. Malanchev and A. D. Lavrukhina and E. Russeil and T. A. Semenikhin and E. Gangler and E. E. O. Ishida and M. V. Pruzhinskaya and A. A. Volnova and S. Sreejith},
journal= {arXiv preprint arXiv:2410.17142},
year = {2024}
}
Comments
13 pages, 1 figure