Method for Combating Uncertainties in Individual's Subjective Affective Judgment

Abstract

The main focus of affective computing has traditionally been on the average of the subjective experiences held by various individuals. Recently, there has been an increase in research on personalization, but it faces issues that were not considered serious when looking at the averages of groups. These issues stem from the uncertainty of subjective judgments themselves, meaning that the same person does not always give the same evaluation to the same situation or object. A framework that trains and evaluates models based on this uncertainty is desired. We introduce a method that unifies the training and evaluation of models, which we call collision probability matching or kappa-matching, to estimate and minimize the potential for improvement in the performance of the models as an absolute measure.