Explainable AI

Current research on explainability and interpretability of machine learning algorithms

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

Alberto Barbado, Óscar Corcho, Richard Benjamins. Expert Systems with Applications Volume 189, 1 March 2022, 116100 2019

[ArXiv]    

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.