Unsupervised Anomaly Detection Ensembles using Item Response Theory
This work addresses a specific problem in anomaly detection for domains lacking labeled data, offering a novel ensemble approach that is incremental in adapting IRT to this context.
The paper tackled the challenge of constructing ensembles for unsupervised anomaly detection without ground truth labels by applying Item Response Theory (IRT) from psychometrics to map and combine heterogeneous methods, resulting in improved performance demonstrated on an extensive data repository.
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.