Data-Efficient and Interpretable Tabular Anomaly Detection

Chun-Hao Chang, Jinsung Yoon, Sercan O Arik, Madeleine Udell, Tomas Pfister

Keywords: Anomaly Detection, Interpretability

TL;DR: We developed an interpretable tabular anomaly detection method that allows incorporation of labeled data.

Abstract: Anomaly detection (AD) plays an important role in numerous applications. In this paper, we focus on two understudied aspects of AD that are critical for integration into real-world applications. First, most AD methods cannot incorporate labeled data that are often available in practice in small quantities and can be crucial to achieve high accuracy. Second, most AD methods are not interpretable, a bottleneck that prevents stakeholders from understanding the reason behind the anomalies. In this paper, we propose a novel AD framework, DIAD, that adapts a white-box model class, Generalized Additive Models, to detect anomalies using a partial identification objective which naturally handles noisy or heterogeneous features. DIAD can incorporate a small amount of labeled data to further boost AD performances in semi-supervised settings. We demonstrate the superiority of DIAD compared to previous work in both unsupervised and semi-supervised settings on multiple datasets. We also present explainability capabilities of DIAD, on its rationale behind predicting certain samples as anomalies.

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Please Choose The Closest Area That Your Submission Falls Into: Unsupervised and Self-supervised learning