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- W3208480178 abstract "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." @default.
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- W3208480178 date "2022-03-01" @default.
- W3208480178 modified "2023-10-04" @default.
- W3208480178 title "Rule extraction in unsupervised anomaly detection for model explainability: Application to OneClass SVM" @default.
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- W3208480178 doi "https://doi.org/10.1016/j.eswa.2021.116100" @default.
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