Affective classification using Bayesian classifier and supervised learning

An affective classification technology plays a key role in the affective human and computer interaction. This paper presents an affective classification method based on the Bayes classifier and the supervisory learning. We newly define a weighted-log-posterior function for the Bayes classifier, instead of the posterior function or the likelihood function that is used in the ordinary Bayes classifier. The weighted-log-posterior function is represented as the weighted sum of likelihood function of each feature plus bias factor under the assumption of feature independence. The Bayes classifier finds an affective state with the maximum value of the weighted-log-posterior function. The weights and the bias factors are iteratively computed by using supervisory learning approach. In the implementation, the affective states are divided into two and three classes in valence dimension and arousal dimension, respectively. An open database for emotion analysis using electroencephalogram (DEAP) is used to evaluate the proposed method. The accuracies for valence and arousal classification are 66.6 % and 66.4 % for two classes and 53.4 % and 51.0 % for three classes, respectively.