A new interpretable unsupervised anomaly detection method based on residual explanation

TL;DR

This paper introduces RXP, an interpretable, low-cost residual explanation method for autoencoder-based anomaly detection, enhancing interpretability and decision support in large-scale critical systems.

Contribution

The paper proposes RXP, a novel residual explanation technique that improves interpretability of autoencoder-based anomaly detection with low computational cost and deterministic outputs.

Findings

RXP outperforms SHAP in real railway data experiments.

RXP provides simple, deterministic explanations for anomalies.

The method is suitable for large-scale, critical systems.

Abstract

Despite the superior performance in modeling complex patterns to address challenging problems, the black-box nature of Deep Learning (DL) methods impose limitations to their application in real-world critical domains. The lack of a smooth manner for enabling human reasoning about the black-box decisions hinder any preventive action to unexpected events, in which may lead to catastrophic consequences. To tackle the unclearness from black-box models, interpretability became a fundamental requirement in DL-based systems, leveraging trust and knowledge by providing ways to understand the model's behavior. Although a current hot topic, further advances are still needed to overcome the existing limitations of the current interpretability methods in unsupervised DL-based models for Anomaly Detection (AD). Autoencoders (AE) are the core of unsupervised DL-based for AD applications, achieving best-in-class performance. However, due to their hybrid aspect to obtain the results (by requiring additional calculations out of network), only agnostic interpretable methods can be applied to AE-based AD. These agnostic methods are computationally expensive to process a large number of parameters. In this paper we present the RXP (Residual eXPlainer), a new interpretability method to deal with the limitations for AE-based AD in large-scale systems. It stands out for its implementation simplicity, low computational cost and deterministic behavior, in which explanations are obtained through the deviation analysis of reconstructed input features. In an experiment using data from a real heavy-haul railway line, the proposed method achieved superior performance compared to SHAP, demonstrating its potential to support decision making in large scale critical systems.