Predicting group emotion in kindergarten classes by modular Bayesian networks

Sung Bae Cho, Jun Ho Kim

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

2 Citations (Scopus)

Abstract

Conventional methods predict emotion directly by measuring equipment like electrode. However, this approach is not suitable for education, especially for children. In this paper, we propose modular Bayesian networks for predicting the emotion with the environment information from the sensors. The Bayesian network is constructed as modules divided by Markov boundary. To evaluate the proposed method, we use data collected from kindergarten classes. The results show more than 84% accuracy and 20 times faster than the single Bayesian network.

Original languageEnglish
Title of host publicationProceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015
EditorsMario Koppen, Azah Kamilah Muda, Kun Ma, Bing Xue, Hideyuki Takagi, Ajith Abraham
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages298-302
Number of pages5
ISBN (Electronic)9781467393607
DOIs
Publication statusPublished - 2016 Jun 15
Event7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015 - Fukuoka, Japan
Duration: 2015 Nov 132015 Nov 15

Publication series

NameProceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015

Other

Other7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015
CountryJapan
CityFukuoka
Period15/11/1315/11/15

Fingerprint

Bayesian networks
Bayesian Networks
Measuring instruments
Electrode
Education
Predict
Module
Sensor
Electrodes
Evaluate
Sensors
Class
Emotion

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Control and Optimization
  • Modelling and Simulation

Cite this

Cho, S. B., & Kim, J. H. (2016). Predicting group emotion in kindergarten classes by modular Bayesian networks. In M. Koppen, A. K. Muda, K. Ma, B. Xue, H. Takagi, & A. Abraham (Eds.), Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015 (pp. 298-302). [7492825] (Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SOCPAR.2015.7492825
Cho, Sung Bae ; Kim, Jun Ho. / Predicting group emotion in kindergarten classes by modular Bayesian networks. Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015. editor / Mario Koppen ; Azah Kamilah Muda ; Kun Ma ; Bing Xue ; Hideyuki Takagi ; Ajith Abraham. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 298-302 (Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015).
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abstract = "Conventional methods predict emotion directly by measuring equipment like electrode. However, this approach is not suitable for education, especially for children. In this paper, we propose modular Bayesian networks for predicting the emotion with the environment information from the sensors. The Bayesian network is constructed as modules divided by Markov boundary. To evaluate the proposed method, we use data collected from kindergarten classes. The results show more than 84{\%} accuracy and 20 times faster than the single Bayesian network.",
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Cho, SB & Kim, JH 2016, Predicting group emotion in kindergarten classes by modular Bayesian networks. in M Koppen, AK Muda, K Ma, B Xue, H Takagi & A Abraham (eds), Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015., 7492825, Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015, Institute of Electrical and Electronics Engineers Inc., pp. 298-302, 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015, Fukuoka, Japan, 15/11/13. https://doi.org/10.1109/SOCPAR.2015.7492825

Predicting group emotion in kindergarten classes by modular Bayesian networks. / Cho, Sung Bae; Kim, Jun Ho.

Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015. ed. / Mario Koppen; Azah Kamilah Muda; Kun Ma; Bing Xue; Hideyuki Takagi; Ajith Abraham. Institute of Electrical and Electronics Engineers Inc., 2016. p. 298-302 7492825 (Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015).

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

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N2 - Conventional methods predict emotion directly by measuring equipment like electrode. However, this approach is not suitable for education, especially for children. In this paper, we propose modular Bayesian networks for predicting the emotion with the environment information from the sensors. The Bayesian network is constructed as modules divided by Markov boundary. To evaluate the proposed method, we use data collected from kindergarten classes. The results show more than 84% accuracy and 20 times faster than the single Bayesian network.

AB - Conventional methods predict emotion directly by measuring equipment like electrode. However, this approach is not suitable for education, especially for children. In this paper, we propose modular Bayesian networks for predicting the emotion with the environment information from the sensors. The Bayesian network is constructed as modules divided by Markov boundary. To evaluate the proposed method, we use data collected from kindergarten classes. The results show more than 84% accuracy and 20 times faster than the single Bayesian network.

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Cho SB, Kim JH. Predicting group emotion in kindergarten classes by modular Bayesian networks. In Koppen M, Muda AK, Ma K, Xue B, Takagi H, Abraham A, editors, Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 298-302. 7492825. (Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015). https://doi.org/10.1109/SOCPAR.2015.7492825