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Affective state classification using Bayesian classifier

Ghazali, Aimi Shazwani and Sidek, Shahrul Na'im and Wok, Saodah (2014) Affective state classification using Bayesian classifier. In: 2014 Fifth International Conference on Intelligent Systems, Modelling and Simulation (ISMS 2014), 27-29 Jan. 2014, Langkawi, Malaysia.

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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.

Item Type: Conference or Workshop Item (Other)
Additional Information: 3028/38240
Uncontrolled Keywords: machine learning system, Bayesian network, affective state, emotion detection
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA164 Bioengineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Mechatronics Engineering
Kulliyyah of Islamic Revealed Knowledge and Human Sciences > Department of Communication
Depositing User: Dr. Shahrul Naim Sidek
Date Deposited: 12 Sep 2014 12:03
Last Modified: 10 Jan 2019 13:02
URI: http://irep.iium.edu.my/id/eprint/38240

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