Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVM
This work addresses the need for explainability in unsupervised learning models, particularly for anomaly detection, which is an incremental step in extending XAI techniques to this domain.
The paper tackles the black box problem in OneClass SVM for unsupervised anomaly detection by evaluating and adapting rule extraction techniques to provide explainability, and proposes algorithms to compute XAI metrics for the extracted rules, demonstrating their application on various datasets including real-world industry data.
OneClass SVM is a popular method for unsupervised anomaly detection. As many other methods, it suffers from the black box problem: it is difficult to justify, in an intuitive and simple manner, why the decision frontier is identifying data points as anomalous or non anomalous. Such type of problem is being widely addressed for supervised models. However, it is still an uncharted area for unsupervised learning. In this paper, we evaluate several rule extraction techniques over OneClass SVM models, as well as present alternative designs for some of those algorithms. Together with that, we propose algorithms to compute metrics related with eXplainable Artificial Intelligence (XAI) regarding the "comprehensibility", "representativeness", "stability" and "diversity" of the extracted rules. We evaluate our proposals with different datasets, including real-world data coming from industry. With this, our proposal contributes to extend XAI techniques to unsupervised machine learning models.