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 pred...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English English |
| Published: |
2014
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| Subjects: | |
| 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 |
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| Institution: | Universiti Islam Antarabangsa Malaysia |
| Language: | English English English |
| 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. |
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