Binarized Support Vector Machines

By Emilio Carrizosa, Belén Martín-Barragán, Dolores Romero Morales. 2010

45 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering, Optimization & Training

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Citations in the corpus

Please Stop Explaining Black Box Models for High Stakes Decisions (2018, background): “Major improvements have been made in the last decade [e.g., see Carrizosa et al., 2010, Su et al.]”
Strongly agree or strongly disagree?: Rating features in Support Vector Machines (2016, background): “…to increasing its sparsity, i.e., to reducing the number of active features in the classi er (Guyon et al. 2002), discretizing the features to detect active ranges of the features (Liu et al. 2002, Romero Morales and Wang 2009) or relevant thresholds for the features (Carrizosa et al. 2010).”
Supersparse linear integer models for optimized medical scoring systems (2015, methodology): “In addition, Carrizosa et al. (2016) consider training linear classifiers with small integer coefficients using a MIP formulation. SLIM can reproduce both of these models. The converse, however, is not true because the methods of Chevaleyre et al. (2013) and Carrizosa et al. (2016): (i) optimize the”
Methods and Models for Interpretable Linear Classification (2014, background): “[25] and Goh and Rudin [44] take different but related approaches for constructing classifiers with binary threshold rules.”
Supervised classification and mathematical optimization (2013, methodology): “In the pursue of interpretability, the so-called Binarized SVM introduced in [48] is trained with features of the form φ`,b as defined in (17).”
Supersparse Linear Integer Models for Interpretable Classification (2013, background): “Carrizosa, Mart n-Barrag an and Morales [12, 13] and Carrizosa, Nogales-G omez and Romero Morales [14] suggest elegant ways to improve the interpretability of SVM classiﬁers by limiting coeﬃcients to a very small set of meaningful values.”
On clustering categories of categorical predictors in generalized linear models (2021, methodology): “We end noting that the methodology proposed in this section can be easily extended to discretize continuous predictors (Carrizosa et al., 2010) or to have as base model a regularized linear model, such as the LASSO or the Group LASSO.”
Optimization Models for Machine Learning: A Survey (2019, background): “Since the classification in high dimensional space can be difficult to interpret for practitioners, Binarized SVM (BSVM) replaces the continuous predictor variables with a linear combination of binary cutoff variables [55].”
Clustering Categories in Support Vector Machines (2017, methodology): “combined methodology could be performed in order to transform continuous features into categorical ones, by applying the techniques from [5, 20], either binarizing or discretizing continuous features and then applying the CLSVM methodology.”