Unsupervised Anomaly Detection Ensembles using Item Response Theory

TL;DR

This paper introduces a novel unsupervised anomaly detection ensemble method based on Item Response Theory, effectively combining diverse detectors without ground truth labels to improve detection accuracy.

Contribution

The paper applies Item Response Theory to unsupervised anomaly detection ensembles, enabling the combination of heterogeneous methods without labeled data.

Findings

IRT-based ensemble outperforms traditional methods

Effective in noisy and heterogeneous detection environments

Enhances detection accuracy by emphasizing sharper methods

Abstract

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.