# Unsupervised Anomaly Detection Ensembles using Item Response Theory

arXiv: https://arxiv.org/abs/2106.06243 · HTML: https://mlsanity.com/paper/2106.06243/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 39 references that are in the map.
- First posted on arXiv 11 Jun 2021.

## Builds on (most cited first, 3 of 3)

- [β3-IRT: A New Item Response Model and its Applications](https://mlsanity.com/paper/1903.04016/) (2019)
- [Anomaly Detection in High Dimensional Data](https://mlsanity.com/paper/1908.04000/) (2019)
- [Item Response Theory based Ensemble in Machine Learning](https://mlsanity.com/paper/1911.04616/) (2019)

## Built on by (most cited first, 2 of 2)

- [Comprehensive Algorithm Portfolio Evaluation using Item Response Theory](https://mlsanity.com/paper/2307.15850/) (2023)
- [On Evaluation of Vision Datasets and Models using Human Competency Frameworks](https://mlsanity.com/paper/2409.04041/) (2024)

## Abstract (arXiv)

Constructing an ensemble from a heterogeneous set of unsupervised anomaly detection methods is challenging because the class labels or the ground truth is unknown. Thus, traditional ensemble techniques that use the response variable or the class labels cannot be used to construct an ensemble for unsupervised anomaly detection. We use Item Response Theory (IRT) -- a class of models used in educational psychometrics to assess student and test question characteristics -- to construct an unsupervised anomaly detection ensemble. IRT's latent trait computation lends itself to anomaly detection because the latent trait can be used to uncover the hidden ground truth. Using a novel IRT mapping to the anomaly detection problem, we construct an ensemble that can downplay noisy, non-discriminatory methods and accentuate sharper methods. We demonstrate the effectiveness of the IRT ensemble on an extensive data repository, by comparing its performance to other ensemble techniques.