# Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection

arXiv: https://arxiv.org/abs/2101.11560 · HTML: https://mlsanity.com/paper/2101.11560/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 72 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 9 of 9)

- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [Toward Supervised Anomaly Detection](https://mlsanity.com/paper/1401.6424/) (2014)
- [XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning](https://mlsanity.com/paper/1912.00290/) (2019)
- [Robust Contextual Outlier Detection: Where Context Meets Sparsity](https://mlsanity.com/paper/1607.08329/) (2016)
- [Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning](https://mlsanity.com/paper/1901.08930/) (2019)
- [Sequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective](https://mlsanity.com/paper/1609.05528/) (2016)
- [Incorporating Feedback into Tree-based Anomaly Detection](https://mlsanity.com/paper/1708.09441/) (2017)
- [No Free Lunch But A Cheaper Supper: A General Framework for Streaming Anomaly Detection](https://mlsanity.com/paper/1909.06927/) (2019)
- [Learning Representations for Outlier Detection on a Budget](https://mlsanity.com/paper/1507.08104/) (2015)

## Built on by (most cited first, 6 of 6)

- [Explainable Contextual Anomaly Detection using Quantile Regression Forests](https://mlsanity.com/paper/2302.11239/) (2023)
- [Explainable Predictive Maintenance](https://mlsanity.com/paper/2306.05120/) (2023)
- [Exploring the impact of Optimised Hyperparameters on Bi-LSTM-based Contextual Anomaly Detector](https://mlsanity.com/paper/2501.15053/) (2025)
- [Detecting Anomalies Using Rotated Isolation Forest](https://mlsanity.com/paper/2501.17787/) (2025)
- [Real-Time Anomaly Detection with Synthetic Anomaly Monitoring (SAM)](https://mlsanity.com/paper/2501.18417/) (2025)
- [Multi-Agent Debate: A Unified Agentic Framework for Tabular Anomaly Detection](https://mlsanity.com/paper/2602.14251/) (2026)

## Abstract (arXiv)

In contextual anomaly detection, an object is only considered anomalous within a specific context. Most existing methods for CAD use a single context based on a set of user-specified contextual features. However, identifying the right context can be very challenging in practice, especially in datasets, with a large number of attributes. Furthermore, in real-world systems, there might be multiple anomalies that occur in different contexts and, therefore, require a combination of several "useful" contexts to unveil them. In this work, we leverage active learning and ensembles to effectively detect complex contextual anomalies in situations where the true contextual and behavioral attributes are unknown. We propose a novel approach, called WisCon (Wisdom of the Contexts), that automatically creates contexts from the feature set. Our method constructs an ensemble of multiple contexts, with varying importance scores, based on the assumption that not all useful contexts are equally so. Experiments show that WisCon significantly outperforms existing baselines in different categories (i.e., active classifiers, unsupervised contextual and non-contextual anomaly detectors, and supervised classifiers) on seven datasets. Furthermore, the results support our initial hypothesis that there is no single perfect context that successfully uncovers all kinds of contextual anomalies, and leveraging the "wisdom" of multiple contexts is necessary.