# PARs: Predicate-based Association Rules for Efficient and Accurate Model-Agnostic Anomaly Explanation

arXiv: https://arxiv.org/abs/2312.10968 · HTML: https://mlsanity.com/paper/2312.10968/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Adversarially Learned Anomaly Detection](https://mlsanity.com/paper/1812.02288/) (2018)
- [Extended Isolation Forest](https://mlsanity.com/paper/1811.02141/) (2018)
- [Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian Filtering](https://mlsanity.com/paper/2106.07992/) (2021)
- [GAN Ensemble for Anomaly Detection](https://mlsanity.com/paper/2012.07988/) (2020)
- [Contextual Outlier Interpretation](https://mlsanity.com/paper/1711.10589/) (2017)
- [A new effective and efficient measure for outlying aspect mining](https://mlsanity.com/paper/2004.13550/) (2020)
- [A Comprehensive Survey on Outlying Aspect Mining Methods](https://mlsanity.com/paper/2005.02637/) (2020)
- [RelSen: An Optimization-based Framework for Simultaneously Sensor Reliability Monitoring and Data Cleaning](https://mlsanity.com/paper/2004.08762/) (2020)

## Abstract (arXiv)

While new and effective methods for anomaly detection are frequently introduced, many studies prioritize the detection task without considering the need for explainability. Yet, in real-world applications, anomaly explanation, which aims to provide explanation of why specific data instances are identified as anomalies, is an equally important task. In this work, we present a novel approach for efficient and accurate model-agnostic anomaly explanation for tabular data using Predicate-based Association Rules (PARs). PARs can provide intuitive explanations not only about which features of the anomaly instance are abnormal, but also the reasons behind their abnormality. Our user study indicates that the anomaly explanation form of PARs is better comprehended and preferred by regular users of anomaly detection systems as compared to existing model-agnostic explanation options. Furthermore, we conduct extensive experiments on various benchmark datasets, demonstrating that PARs compare favorably to state-of-the-art model-agnostic methods in terms of computing efficiency and explanation accuracy on anomaly explanation tasks. The code for PARs tool is available at https://github.com/NSIBF/PARs-EXAD.