Title :
Affective classification using Bayesian classifier and supervised learning
Author :
Chung, Seong Youb ; Yoon, Hyun Joong
Author_Institution :
Dept. of Mech. Eng., Korea Nat. Univ. of Transp., Chungju, South Korea
Abstract :
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.
Keywords :
Bayes methods; electroencephalography; emotion recognition; human computer interaction; iterative methods; learning (artificial intelligence); maximum likelihood estimation; medical signal processing; pattern classification; signal classification; Bayesian classifier; DEAP; affective classification; arousal classification; arousal dimension; bias factors; electroencephalogram; emotion analysis; feature independence; human computer interaction; likelihood function; supervised learning; valence classification; valence dimension; weighted sum; weighted-log-posterior function; Accuracy; Bayesian methods; Electroencephalography; Emotion recognition; Humans; Support vector machine classification; Affective classification; Bayes classifier; electroencephalogram; supervised learning;
Conference_Titel :
Control, Automation and Systems (ICCAS), 2012 12th International Conference on
Conference_Location :
JeJu Island
Print_ISBN :
978-1-4673-2247-8