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

Kyon Mo Yang, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (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 languageEnglish
Title of host publication2014 10th International Conference on Natural Computation, ICNC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1131-1136
Number of pages6
ISBN (Electronic)9781479951505
DOIs
Publication statusPublished - 2014 Jan 1
Event2014 10th International Conference on Natural Computation, ICNC 2014 - Xiamen, China
Duration: 2014 Aug 192014 Aug 21

Publication series

Name2014 10th International Conference on Natural Computation, ICNC 2014

Other

Other2014 10th International Conference on Natural Computation, ICNC 2014
CountryChina
CityXiamen
Period14/8/1914/8/21

Fingerprint

Bayesian networks
Marketing
Education
Experiments
Uncertainty

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

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
Yang, Kyon Mo ; Cho, Sung Bae. / Modular dynamic bayesian network based on markov boundary for emotion prediction in multi-sensory environment. 2014 10th International Conference on Natural Computation, ICNC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 1131-1136 (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 proceedingConference contribution

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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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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). https://doi.org/10.1109/ICNC.2014.6976000