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

In linear classiers, such as the Support Vector Machines (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 Machines (DILSVM) where the feature scores can only take on a discrete number of values, dened by the so-called feature rating levels. The DILSVM classier benets from interpretability as it can be seen as a collection of Likert scales, one for each feature, where we rate the level of agreement with the positive class. To build the DILSVM classier, we propose a Mixed Integer Linear Programming approach, as well as a collection of strategies to reduce the building times. Our computational experience shows that the 3-point and the 5-point DILSVM classiers have comparable accuracy to the SVM with a substanti...

• Type: Journal
• Year: 2016
• Where: ISCI
• Authors: Emilio Carrizosa, Amaya Nogales-Gómez, Dolores Romero Morales
• Keywords: Artificial Intelligence, ISCI 2016