# Coniferest: a complete active anomaly detection framework

arXiv: https://arxiv.org/abs/2410.17142 · HTML: https://mlsanity.com/paper/2410.17142/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 22 Oct 2024.

## Builds on (most cited first, 3 of 3)

- [Deep Anomaly Detection with Deviation Networks](https://mlsanity.com/paper/1911.08623/) (2019)
- [Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning](https://mlsanity.com/paper/1901.08930/) (2019)
- [Active Anomaly Detection via Ensembles](https://mlsanity.com/paper/1809.06477/) (2018)

## Built on by (most cited first, 1 of 1)

- [Signatures to help interpretability of anomalies](https://mlsanity.com/paper/2506.16314/) (2025)

## Abstract (arXiv)

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.