Affective state classification using Bayesian classifier

• Main Authors: Ghazali, Aimi Shazwani, Sidek, Shahrul Na'im, Wok, Saodah
• Format: Conference or Workshop Item
• Language: English; English; English
• Published: 2014
• Subjects: TA164 Bioengineering
• Online Access: http://irep.iium.edu.my/38240/1/official_isms_paper.pdf; http://irep.iium.edu.my/38240/2/naim_uksim.pdf; http://irep.iium.edu.my/38240/8/38240_Affective%20state%20classification%20using%20Bayesian_Scopus.pdf; http://irep.iium.edu.my/38240/; http://uksim.info/isms2014/CD+ToC.pdf
• Institution: Universiti Islam Antarabangsa Malaysia

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.