Affective State Classification Using Bayesian Classifier - Ghazali, Aimi Shazwani; Sidek, Shahrul Naim; Saodah Wok (Proceedings (International Conference on Intelligent Systems, Modelling and Simulation.), 2014)
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.