Eclectic Rule-Extraction from Support Vector Machines

By Nahla H. Barakat, Joachim Diederich. 2005

157 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, positive): “In [57] the same authors propose eclectic rule extraction, still considering only the support vectors of a trained model.”
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018, background): “The third type (eclectic) is a hybrid approach for rule extraction that incorporates elements of both the decompositionnel and pedagogical rule-extraction techniques [120].”
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.”
Extended Taxonomy of Rule Extraction Techniques and Assessment of KDRuleEx (2012, background): “If Rule Extraction techniques incorporate elements of both pedagogical and decompositional then such techniques known as eclectic techniques [35] and DEDEC is popular example of this kind [21].”
Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring (2008, methodology): “A slightly diﬀerent variant is proposed in [6], where only the support vectors are used.”
Evaluating XAI: A comparison of rule-based and example-based explanations (2021, background): “In addition, this practice allows results to be meaningful even if hypotheses are rejected, as they falsify a scientiﬁc theory that may have been accepted as true.”
Rule extraction from support vector machines: A review (2010, methodology): “However, the methods described in [33] create the artificial data set from the SVs only.”
A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue (2007, methodology): “Following the training of SVM, an interpretable model such as decision tree [44,45] is used to generate rules.”
Binarized Support Vector Machines (2010, methodology): “Binarizing continuous predictor variables has also been proposed in the so-called rule extraction procedures, within SVM [3, 4, 16, 27, 29] and Neural Networks as well, e.”