On Predictive Explanation of Data Anomalies

TL;DR

This paper introduces PROTEUS, an AutoML pipeline that creates surrogate models for explaining anomaly detection decisions by selecting key features, enabling human-understandable visualizations and reliable out-of-sample predictions.

Contribution

PROTEUS is a novel AutoML-based approach that produces predictive explanations for anomaly detectors, especially effective on high-dimensional and imbalanced datasets.

Findings

PROTEUS accurately explains various anomaly detection models.

It reliably estimates out-of-sample predictive performance.

The method is robust to high-dimensional data.

Abstract

Numerous algorithms have been proposed for detecting anomalies (outliers, novelties) in an unsupervised manner. Unfortunately, it is not trivial, in general, to understand why a given sample (record) is labelled as an anomaly and thus diagnose its root causes. We propose the following reduced-dimensionality, surrogate model approach to explain detector decisions: approximate the detection model with another one that employs only a small subset of features. Subsequently, samples can be visualized in this low-dimensionality space for human understanding. To this end, we develop PROTEUS, an AutoML pipeline to produce the surrogate model, specifically designed for feature selection on imbalanced datasets. The PROTEUS surrogate model can not only explain the training data, but also the out-of-sample (unseen) data. In other words, PROTEUS produces predictive explanations by approximating the decision surface of an unsupervised detector. PROTEUS is designed to return an accurate estimate of out-of-sample predictive performance to serve as a metric of the quality of the approximation. Computational experiments confirm the efficacy of PROTEUS to produce predictive explanations for different families of detectors and to reliably estimate their predictive performance in unseen data. Unlike several ad-hoc feature importance methods, PROTEUS is robust to high-dimensional data.