# Incorporating Feedback into Tree-based Anomaly Detection

arXiv: https://arxiv.org/abs/1708.09441 · HTML: https://mlsanity.com/paper/1708.09441/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2017.

## Built on by (most cited first, 7 of 7)

- [Data-Efficient and Interpretable Tabular Anomaly Detection](https://mlsanity.com/paper/2203.02034/) (2022)
- [Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection](https://mlsanity.com/paper/2101.11560/) (2021)
- [Active anomaly detection based on deep one-class classification](https://mlsanity.com/paper/2309.09465/) (2023)
- [TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenarios](https://mlsanity.com/paper/2111.15432/) (2021)
- [Exploring the Universe with SNAD: Anomaly Detection in Astronomy](https://mlsanity.com/paper/2410.18875/) (2024)
- [Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems](https://mlsanity.com/paper/2207.03934/) (2022)
- [Expert enhanced dynamic time warping based anomaly detection](https://mlsanity.com/paper/2310.02280/) (2023)

## Abstract (arXiv)

Anomaly detectors are often used to produce a ranked list of statistical anomalies, which are examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, in realworld applications, this process can be exceedingly difficult for the analyst since a large fraction of high-ranking anomalies are false positives and not interesting from the application perspective. In this paper, we aim to make the analyst's job easier by allowing for analyst feedback during the investigation process. Ideally, the feedback influences the ranking of the anomaly detector in a way that reduces the number of false positives that must be examined before discovering the anomalies of interest. In particular, we introduce a novel technique for incorporating simple binary feedback into tree-based anomaly detectors. We focus on the Isolation Forest algorithm as a representative tree-based anomaly detector, and show that we can significantly improve its performance by incorporating feedback, when compared with the baseline algorithm that does not incorporate feedback. Our technique is simple and scales well as the size of the data increases, which makes it suitable for interactive discovery of anomalies in large datasets.