Rule extraction from linear support vector machines

By glenn. fung, Sathyakama Sandilya, R. Rao. 2005

171 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, positive): “In [105] , the authors formulate the rule extraction problem as a multi-constrained optimization to create a set of non-overlapping rules.”
Local Rule-Based Explanations of Black Box Decision Systems (2018, background): “Classification rules have been widely used to explain neural networks [2, 3, 21] but also to understand the global behavior of SVMs [15, 27].”
A Survey of Methods for Explaining Black Box Models (2018, methodology): “, in Reference [33], describe as function f an algorithm based on constraint programming for converting linear SVM b (and other hyperplane-based linear classifiers) into a set of non”
Comprehensible credit scoring models using rule extraction from support vector machines (2007, neutral): “extract non-overlapping rules by constructing hypercubes with axis-parallel surfaces [11].”
Methods and Models for Interpretable Linear Classification (2014, methodology): “The interpretability of black-box models is mainly improved by auxiliary methods that extract rules and prototype examples to illustrate the relationship between input variables and the predicted outcome [78, 111, 40, 76, 11].”
Meaningful Explanations of Black Box AI Decision Systems (2019, neutral): “For example, decision trees have been adopted to explain neural networks (Krishnan, Sivakumar, and Bhat-tacharya 1999) and tree ensembles (Tan, Hooker, and Wells 2016), while decision rules have been widely used to explain neural networks (Augasta and Kathirvalavakumar 2012; Andrews, Diederich, and ”
Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring (2008, methodology): “In [25], Fung et al. present an algorithm to extract propositional classiﬁcation rules from linear classiﬁers.”
Open the Black Box Data-Driven Explanation of Black Box Decision Systems (2018, methodology): “For example, decision trees have been adopted to explain neural networks [26] and tree ensembles [22, 42], while decision rules have been widely used to explain neural networks [1, 2] and support vector machines [16].”
Binarized Support Vector Machines (2010, methodology): “Binarizing continuous predictor variables has also been proposed in the socalled rule extraction procedures within SVM (Barakat and Diederich 2004, 2005; Fung et al. 2005; Martens et al. 2007; Núñez et al. 2002) and neural networks (Andrews et al.”
Rule extraction from support vector machines: A review (2010, methodology): “’s [13] which solve multiple optimization problems to find rules that described nonempty hyper-cubes in each class half-space.”
Explainable Artificial Intelligence: a Systematic Review (2020, background): “ExtractRule [138] converts hyperplane-based linear classifiers, like SVMs, into a set of nonoverlapping symbolic rules which are human-understandable because they display, in a compact format, the inferential process of the underlying classifier.”
A Bayesian Approach to Learning Scoring Systems (2015, methodology): “Other methods (Meinshausen, 2010; Van Assche and Blockeel, 2007; Fung, Sandilya, and Rao, 2005; Martens et al., 2007; Bien and Tibshirani, 2011) extract rules and prototype examples to illustrate the relationship between input variables and the predicted outcome.”
Efﬁcient Filtering on Hidden Document Streams (unknown): “Many other types of classiﬁers like decision trees (Quinlan 1987) and linear classiﬁers (Towell and Shavlik 1993; Fung, Sandilya, and Rao 2005) can be easily converted to sets of rules; hence we believe that our modeling is quite general.”
Rule Extraction from Support Vector Machines: A Sequential Covering Approach (2007, methodology): “It is worth noting that although the primary justification for applying rule extraction from SVMs was to provide additional explanatory power, the extracted rules can also be used independently for classification as they have excellent performance compared to the most commonly used rule learning alg”
Efficient Filtering on Hidden Document Streams (2014, background): “" Many other types of classifiers like decision trees (Quinlan 1987) and linear classifiers (Towell and Shavlik 1993; Fung, Sandilya, and Rao 2005) can be easily converted to sets of rules; hence we believe that our modeling is quite general.”
A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue (2007, methodology): “In [42], the mathematical programming is use to optimize the vertexes of rules.”
Interpretable Rule Discovery Through Bilevel Optimization of Split-Rules of Nonlinear Decision Trees for Classification Problems (2020, methodology): “The proposed approach outperformed the classifiers generated using techniques proposed in [12], [17], [16], [13], [14] in terms of both: accuracy and comprehensibility/compactness.”