Rule Extraction from Trained Support Vector Machines

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Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Similarly in [106] , the authors proposed the so-called Hyper-rectangle Rule Extraction, an algorithm based on SVC (Support Vector Clustering) to ﬁnd prototype vectors for each class and then deﬁne small hyper-rectangles around.”
Rule Extraction from Support Vector Machines: A Sequential Covering Approach (2007, methodology): “Therefore, several methods have been introduced for rule extraction from both ANNs [2] and, more recently, SVMs [1], [3], [4], [5], [6], [7], [8], which will be summarized in Section 4.”
Rule extraction from support vector machines: A review (2010, methodology): “In (SVM+) [25–28] and ‘HRE’ [29], the authors used clustering algorithms to define prototype vectors which are then utilized with SVs to construct the region, while in ‘RulExSVM’ [30], the algorithm utilizes the SVM decision hyper-curve and the support vectors to construct hyper-rectangles.”
Explaining prediction models and individual predictions with feature contributions (2014, background): “Complex models, such as artificial neural networks [14, 15, 16, 17, 18] and SVM (Support Vector Machines) [19, 20, 10, 21, 22, 23, 24, 25] received most attention, because they are often very successful but non-transparent.”