Affective State Classification Using Bayesian Classifier

• Published in: Proceedings (International Conference on Intelligent Systems, Modelling and Simulation.) pp. 154 - 158
• Main Authors: Ghazali, Aimi Shazwani, Sidek, Shahrul Naim, Saodah Wok
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.01.2014
• Subjects: affective state; Bayes methods; Bayesian network; emotion detection; Emotion recognition; Learning (artificial intelligence); machine learning system; Robots; Software; Support vector machines; Training
• ISSN: 2166-0662
• DOI: 10.1109/ISMS.2014.32

This paper elaborates the basic structure of a machine learning system in classifying affective state. There are several techniques in classifying the states depending on the type of input-output dataset. A proper selection of techniques is crucial in determining the success rate of the system prediction. The paper proposes a machine learning technique in classifying affective states of human subjects by using Bayesian Network (BN). A structured experimental setup is designed to induce the affective states of the subjects by using a set of audiovisual stimulants. The affective states under study are happy, sad, and nervous. Preliminary results demonstrate the ability of the BN to predict human affective state with 86% accuracy.