Affective State Classification Using Bayesian Classifier

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 predi...

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Bibliographic Details
Published inProceedings (International Conference on Intelligent Systems, Modelling and Simulation.) pp. 154 - 158
Main Authors Ghazali, Aimi Shazwani, Sidek, Shahrul Naim, Saodah Wok
Format Conference Proceeding
LanguageEnglish
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
Online AccessGet full text
ISSN2166-0662
DOI10.1109/ISMS.2014.32

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Summary: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.
ISSN:2166-0662
DOI:10.1109/ISMS.2014.32