Strongly agree or strongly disagree?: Rating features in Support Vector Machines

By Emilio Carrizosa, Amaya Nogales-Gómez, Dolores Romero Morales. 2016

29 citations recorded by the source corpus.

In linear classifiers, such as the Support Vector Machine (SVM), a score is associated with each feature and objects are assigned to classes based on the linear combination of the scores and the values of the features. Inspired by discrete psychometric scales, which measure the extent to which a factor is in agreement with a statement, we propose the Discrete Level Support Vector Machine (DILSVM) where the feature scores can only take on a discrete number of values, defined by the so-called feature rating levels. The DILSVM classifier benefits from interpretability and it has visual appeal, since it can be represented as a collection of Likert scales, one for each feature, where we rate the level of agreement with the positive class. To construct the DILSVM classifier, we propose a Mixed Integer Linear Programming approach, as well as a collection of strategies to reduce computational cost. Our numerical experiments show that the three-point and the five-point DILSVM classifiers have comparable accuracy to the SVM with a substantial gain in interpretability and visual appeal, but also in sparsity, thanks to the appropriate choice of the feature rating levels.

Subfields: Artificial Intelligence

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Citations in the corpus

Learning Optimized Risk Scores (2016, methodology): “Our work is part of a recent stream of research on integer programming in supervised learning (Carrizosa et al., 2016; Liu and Wu, 2007; Goldberg and Eckstein, 2012; Guan et al., 2009; Nguyen and Franke, 2012; Sato et al., 2015, 2016; Bertsimas et al., 2016; Bertsimas and Dunn, 2017).”
Supersparse linear integer models for optimized medical scoring systems (2015, methodology): “SLIM can reproduce the models proposed by Chevaleyre et al. (2013) and Carrizosa et al. (2013) (see e.g., our formulation to create M-of-N rule tables in Section 8.2.1).”
A Bayesian Approach to Learning Scoring Systems (2015, unknown): “The work of Carrizosa et al. 26 is a support vector machine approach, aiming for the middle ground between computational tractability and accuracy in producing scoring systems.”
Clustering Categories in Support Vector Machines (2017, methodology): “First, knowledge domain [7, 18] can be incorporated into the methodology to build a set of comprehensible rules to facilitate interpretability.”