# Feature Explosion: a generic optimization strategy for outlier detection algorithms

arXiv: https://arxiv.org/abs/2502.05496 · HTML: https://mlsanity.com/paper/2502.05496/

## 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 8 Feb 2025.

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

- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [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)
- [Deep Isolation Forest for Anomaly Detection](https://mlsanity.com/paper/2206.06602/) (2022)
- [Extended Isolation Forest](https://mlsanity.com/paper/1811.02141/) (2018)
- [LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks](https://mlsanity.com/paper/2112.05355/) (2021)
- [DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection](https://mlsanity.com/paper/2106.05410/) (2021)
- [Gravitational Clustering](https://mlsanity.com/paper/1509.01659/) (2015)
- [Exploring the Impact of Outlier Variability on Anomaly Detection Evaluation Metrics](https://mlsanity.com/paper/2409.15986/) (2024)
- [Detecting outliers by clustering algorithms](https://mlsanity.com/paper/2412.05669/) (2024)

## Abstract (arXiv)

Outlier detection tasks aim at discovering potential issues or opportunities and are widely used in cybersecurity, financial security, industrial inspection, etc. To date, thousands of outlier detection algorithms have been proposed. Clearly, in real-world scenarios, such a large number of algorithms is unnecessary. In other words, a large number of outlier detection algorithms are redundant. We believe the root cause of this redundancy lies in the current highly customized (i.e., non-generic) optimization strategies. Specifically, when researchers seek to improve the performance of existing outlier detection algorithms, they have to design separate optimized versions tailored to the principles of each algorithm, leading to an ever-growing number of outlier detection algorithms. To address this issue, in this paper, we introduce the explosion from physics into the outlier detection task and propose a generic optimization strategy based on feature explosion, called OSD (Optimization Strategy for outlier Detection algorithms). In the future, when improving the performance of existing outlier detection algorithms, it will be sufficient to invoke the OSD plugin without the need to design customized optimized versions for them. We compared the performances of 14 outlier detection algorithms on 24 datasets before and after invoking the OSD plugin. The experimental results show that the performances of all outlier detection algorithms are improved on almost all datasets. In terms of average accuracy, OSD make these outlier detection algorithms improve by 15% (AUC), 63.7% (AP).