Modular dynamic bayesian network based on markov boundary for emotion prediction in multi-sensory environment

Kyon Mo Yang; Sung Bae Cho

doi:10.1109/ICNC.2014.6976000

Modular dynamic bayesian network based on markov boundary for emotion prediction in multi-sensory environment

Kyon Mo Yang, Sung Bae Cho

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Recently, a lot of the fields such as education, marketing, and design have applied human's emotion stimuli to increase the effectiveness of services as well as user-computer interaction. Predicting the emotion in the field is important to decide relevant stimuli because emotion has the element of uncertainty and is sensitive to sensory stimuli. In this paper, we propose a modular dynamic Bayesian network based on Markov boundary theory to predict current emotion. A relation between emotion and stimuli is identified as four types of structure. The proposed method was verified by several experiments. The computational time is 0.032 second and the average accuracy rate is 80.97%, which are quite promising for a realistic system.

Original language	English
Title of host publication	2014 10th International Conference on Natural Computation, ICNC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1131-1136
Number of pages	6
ISBN (Electronic)	9781479951505
DOIs	https://doi.org/10.1109/ICNC.2014.6976000
Publication status	Published - 2014
Event	2014 10th International Conference on Natural Computation, ICNC 2014 - Xiamen, China Duration: 2014 Aug 19 → 2014 Aug 21

Publication series

Name	2014 10th International Conference on Natural Computation, ICNC 2014

Other

Other	2014 10th International Conference on Natural Computation, ICNC 2014
Country/Territory	China
City	Xiamen
Period	14/8/19 → 14/8/21

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computational Theory and Mathematics
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Signal Processing
Biomedical Engineering

Access to Document

10.1109/ICNC.2014.6976000

Cite this

Yang, K. M., & Cho, S. B. (2014). Modular dynamic bayesian network based on markov boundary for emotion prediction in multi-sensory environment. In 2014 10th International Conference on Natural Computation, ICNC 2014 (pp. 1131-1136). [6976000] (2014 10th International Conference on Natural Computation, ICNC 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICNC.2014.6976000

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title = "Modular dynamic bayesian network based on markov boundary for emotion prediction in multi-sensory environment",

abstract = "Recently, a lot of the fields such as education, marketing, and design have applied human's emotion stimuli to increase the effectiveness of services as well as user-computer interaction. Predicting the emotion in the field is important to decide relevant stimuli because emotion has the element of uncertainty and is sensitive to sensory stimuli. In this paper, we propose a modular dynamic Bayesian network based on Markov boundary theory to predict current emotion. A relation between emotion and stimuli is identified as four types of structure. The proposed method was verified by several experiments. The computational time is 0.032 second and the average accuracy rate is 80.97%, which are quite promising for a realistic system.",

author = "Yang, {Kyon Mo} and Cho, {Sung Bae}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 10th International Conference on Natural Computation, ICNC 2014 ; Conference date: 19-08-2014 Through 21-08-2014",

year = "2014",

doi = "10.1109/ICNC.2014.6976000",

language = "English",

series = "2014 10th International Conference on Natural Computation, ICNC 2014",

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Yang, KM & Cho, SB 2014, Modular dynamic bayesian network based on markov boundary for emotion prediction in multi-sensory environment. in 2014 10th International Conference on Natural Computation, ICNC 2014., 6976000, 2014 10th International Conference on Natural Computation, ICNC 2014, Institute of Electrical and Electronics Engineers Inc., pp. 1131-1136, 2014 10th International Conference on Natural Computation, ICNC 2014, Xiamen, China, 14/8/19. https://doi.org/10.1109/ICNC.2014.6976000

Modular dynamic bayesian network based on markov boundary for emotion prediction in multi-sensory environment. / Yang, Kyon Mo; Cho, Sung Bae.

2014 10th International Conference on Natural Computation, ICNC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1131-1136 6976000 (2014 10th International Conference on Natural Computation, ICNC 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Modular dynamic bayesian network based on markov boundary for emotion prediction in multi-sensory environment

AU - Yang, Kyon Mo

AU - Cho, Sung Bae

PY - 2014

Y1 - 2014

N2 - Recently, a lot of the fields such as education, marketing, and design have applied human's emotion stimuli to increase the effectiveness of services as well as user-computer interaction. Predicting the emotion in the field is important to decide relevant stimuli because emotion has the element of uncertainty and is sensitive to sensory stimuli. In this paper, we propose a modular dynamic Bayesian network based on Markov boundary theory to predict current emotion. A relation between emotion and stimuli is identified as four types of structure. The proposed method was verified by several experiments. The computational time is 0.032 second and the average accuracy rate is 80.97%, which are quite promising for a realistic system.

AB - Recently, a lot of the fields such as education, marketing, and design have applied human's emotion stimuli to increase the effectiveness of services as well as user-computer interaction. Predicting the emotion in the field is important to decide relevant stimuli because emotion has the element of uncertainty and is sensitive to sensory stimuli. In this paper, we propose a modular dynamic Bayesian network based on Markov boundary theory to predict current emotion. A relation between emotion and stimuli is identified as four types of structure. The proposed method was verified by several experiments. The computational time is 0.032 second and the average accuracy rate is 80.97%, which are quite promising for a realistic system.

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M3 - Conference contribution

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T3 - 2014 10th International Conference on Natural Computation, ICNC 2014

SP - 1131

EP - 1136

BT - 2014 10th International Conference on Natural Computation, ICNC 2014

PB - Institute of Electrical and Electronics Engineers Inc.

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Yang KM, Cho SB. Modular dynamic bayesian network based on markov boundary for emotion prediction in multi-sensory environment. In 2014 10th International Conference on Natural Computation, ICNC 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 1131-1136. 6976000. (2014 10th International Conference on Natural Computation, ICNC 2014). doi: 10.1109/ICNC.2014.6976000

Modular dynamic bayesian network based on markov boundary for emotion prediction in multi-sensory environment

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