# XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning

arXiv: https://arxiv.org/abs/1912.00290 · HTML: https://mlsanity.com/paper/1912.00290/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2019.

## Built on by (most cited first, 30 of 42)

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- [The OPS-SAT benchmark for detecting anomalies in satellite telemetry](https://mlsanity.com/paper/2407.04730/) (2024)
- [Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection](https://mlsanity.com/paper/2101.11560/) (2021)
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- [Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms](https://mlsanity.com/paper/2105.12818/) (2021)
- [ODBO: Bayesian Optimization with Search Space Prescreening for Directed Protein Evolution](https://mlsanity.com/paper/2205.09548/) (2022)
- [Comparison of Outlier Detection Techniques for Structured Data](https://mlsanity.com/paper/2106.08779/) (2021)
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- [Negative Selection Algorithm Research and Applications in the last decade: A Review](https://mlsanity.com/paper/2105.06109/) (2021)
- [Outlier Detection using AI: A Survey](https://mlsanity.com/paper/2112.00588/) (2021)
- [Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model](https://mlsanity.com/paper/2204.07014/) (2022)
- [AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on Anomalies](https://mlsanity.com/paper/2305.18798/) (2023)
- [Outlier detection using flexible categorisation and interrogative agendas](https://mlsanity.com/paper/2312.12010/) (2023)
- [Flexible categorization using formal concept analysis and Dempster-Shafer theory](https://mlsanity.com/paper/2408.15012/) (2024)
- [Towards Unsupervised Validation of Anomaly-Detection Models](https://mlsanity.com/paper/2410.14579/) (2024)
- [Memorize Early, Then Query: Inlier-Memorization-Guided Active Outlier Detection](https://mlsanity.com/paper/2601.10993/) (2026)
- [Ymir: A Supervised Ensemble Framework for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2112.04704/) (2021)

## Abstract (arXiv)

A new semi-supervised ensemble algorithm called XGBOD (Extreme Gradient Boosting Outlier Detection) is proposed, described and demonstrated for the enhanced detection of outliers from normal observations in various practical datasets. The proposed framework combines the strengths of both supervised and unsupervised machine learning methods by creating a hybrid approach that exploits each of their individual performance capabilities in outlier detection. XGBOD uses multiple unsupervised outlier mining algorithms to extract useful representations from the underlying data that augment the predictive capabilities of an embedded supervised classifier on an improved feature space. The novel approach is shown to provide superior performance in comparison to competing individual detectors, the full ensemble and two existing representation learning based algorithms across seven outlier datasets.