# Monotonic anomaly detection

arXiv: https://arxiv.org/abs/2410.23158 · HTML: https://mlsanity.com/paper/2410.23158/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 30 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions](https://mlsanity.com/paper/2201.00382/) (2022)
- [COPOD: Copula-Based Outlier Detection](https://mlsanity.com/paper/2009.09463/) (2020)
- [FairOD: Fairness-aware Outlier Detection](https://mlsanity.com/paper/2012.03063/) (2020)
- [Towards Fair Deep Anomaly Detection](https://mlsanity.com/paper/2012.14961/) (2020)
- [Monotonic classification: an overview on algorithms, performance measures and data sets](https://mlsanity.com/paper/1811.07155/) (2018)
- [Fair Outlier Detection](https://mlsanity.com/paper/2005.09900/) (2020)
- [Explainable Contextual Anomaly Detection using Quantile Regression Forests](https://mlsanity.com/paper/2302.11239/) (2023)
- [Average Localised Proximity: A new data descriptor with good default one-class classification performance](https://mlsanity.com/paper/2101.11037/) (2021)
- [Optimised one-class classification performance](https://mlsanity.com/paper/2102.02618/) (2021)

## Abstract (arXiv)

Semi-supervised anomaly detection is based on the principle that any record that looks different from normal training data is a potential anomaly. However, in some cases we are specifically interested in anomalies that correspond to high attribute values (or low, but not both). For distance-based methods, we propose an asymmetrical distance measure that takes this monotonicity into account by incorporating the ramp function. For the Isolation Forest algorithm, we propose a modified path length algorithm. Through experiments on synthetic and real-life datasets, we show that these proposals increase anomaly detection performance on datasets with monotonic attributes.