Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVM

OneClass SVM is a popular method for unsupervised anomaly detection. As many other methods, it suffers from the \textit{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 some of the most important rule extraction techniques over OneClass SVM models, as well as presenting alternative designs for some of those XAI algorithms. Together with that, we propose algorithms to compute metrics related with XAI regarding the "comprehensivility", "representativeness", "stability" and "diversity" of the rules extracted. We evaluate our proposals with different datasets, including real-world data coming from industry. With this, our proposal contributes to extend Explainable AI techniques to unsupervised machine learning models.