Coniferest: a complete active anomaly detection framework
This work provides a tool for researchers and practitioners in fields like astronomy to perform active anomaly detection, but it is incremental as it builds on existing algorithms without introducing new methods.
The authors introduced Coniferest, an open-source Python framework for active anomaly detection, which includes algorithms like Isolation Forest, AAD, and Pineforest, and demonstrated its performance on synthetic datasets and real astronomical data from the SNAD project.
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.