Contextualizing Support Vector Machine Predictions
Marcelo Loor, Guy De Tré · International Journal of Computational Intelligence Systems · 2020
Classification in artificial intelligence is usually understood as a process whereby several objects are evaluated to predict the class(es) those objects belong to.Aiming to improve the interpretability of predictions resulting from a support vector machine classification process, we explore the use of augmented appraisal degrees to put those predictions in context.A use case, in which the classes of handwritten digits are predicted, illustrates how the interpretability of such predictions is benefitted from their contextualization.