Learning bayesian network to predict group emotion in kindergarten by evolutionary computation

Seul Gi Choi; Sung Bae Cho

doi:10.1007/978-3-319-67180-2_1

Learning bayesian network to predict group emotion in kindergarten by evolutionary computation

Seul Gi Choi, Sung Bae Cho

College of Computing

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

Abstract

In the educational services, students’ emotions are an important factor that determine its effect. We have previously conducted research that led them to target emotions using environmental factors. However, the study used the bayesian network based on domain knowledge to predict emotions, which may differ from the actual environment. In this paper, we propose a method to learn the bayesian network for group emotion prediction in kindergarten from data through evolutionary computation. The learning data are brightness, color temperature, sound, volume, smell, temperature, humidity, and current emotion. The structure of the network is encoded with two chromosomes to represent nodes and arcs. To explore the optimal structure, evolutionary operators are used that can convey information in sets. We also experiment with various inference nodes not observed. Experimental results show that the accuracy is 85% with 20 inference nodes, which can replace network designed with domain knowledge. By comparing the evolution of the best model, we analyze the influential factors that determine the structure.

Original language	English
Title of host publication	International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings
Editors	Hilde Perez Garcia, Javier Alfonso-Cendon, Lidia Sanchez Gonzalez, Emilio Corchado, Hector Quintian
Publisher	Springer Verlag
Pages	3-12
Number of pages	10
ISBN (Print)	9783319671796
DOIs	https://doi.org/10.1007/978-3-319-67180-2_1
Publication status	Published - 2018
Event	International Joint Conference on 12th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2017, 10th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2017 and 8th International Conference on European Transnational Education, ICEUTE 2017 - Leon, Spain Duration: 2017 Sept 6 → 2017 Sept 8

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	649
ISSN (Print)	2194-5357

Other

Other	International Joint Conference on 12th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2017, 10th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2017 and 8th International Conference on European Transnational Education, ICEUTE 2017
Country/Territory	Spain
City	Leon
Period	17/9/6 → 17/9/8

Bibliographical note

Funding Information:
Acknowledgements. This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract (UD160066BD).

Publisher Copyright:
© 2018, Springer International Publishing AG.

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Computer Science(all)

Access to Document

10.1007/978-3-319-67180-2_1

Cite this

Choi, S. G., & Cho, S. B. (2018). Learning bayesian network to predict group emotion in kindergarten by evolutionary computation. In H. Perez Garcia, J. Alfonso-Cendon, L. Sanchez Gonzalez, E. Corchado, & H. Quintian (Eds.), International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings (pp. 3-12). (Advances in Intelligent Systems and Computing; Vol. 649). Springer Verlag. https://doi.org/10.1007/978-3-319-67180-2_1

Choi, Seul Gi ; Cho, Sung Bae. / Learning bayesian network to predict group emotion in kindergarten by evolutionary computation. International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings. editor / Hilde Perez Garcia ; Javier Alfonso-Cendon ; Lidia Sanchez Gonzalez ; Emilio Corchado ; Hector Quintian. Springer Verlag, 2018. pp. 3-12 (Advances in Intelligent Systems and Computing).

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abstract = "In the educational services, students{\textquoteright} emotions are an important factor that determine its effect. We have previously conducted research that led them to target emotions using environmental factors. However, the study used the bayesian network based on domain knowledge to predict emotions, which may differ from the actual environment. In this paper, we propose a method to learn the bayesian network for group emotion prediction in kindergarten from data through evolutionary computation. The learning data are brightness, color temperature, sound, volume, smell, temperature, humidity, and current emotion. The structure of the network is encoded with two chromosomes to represent nodes and arcs. To explore the optimal structure, evolutionary operators are used that can convey information in sets. We also experiment with various inference nodes not observed. Experimental results show that the accuracy is 85% with 20 inference nodes, which can replace network designed with domain knowledge. By comparing the evolution of the best model, we analyze the influential factors that determine the structure.",

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note = "Funding Information: Acknowledgements. This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract (UD160066BD). Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG.; International Joint Conference on 12th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2017, 10th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2017 and 8th International Conference on European Transnational Education, ICEUTE 2017 ; Conference date: 06-09-2017 Through 08-09-2017",

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Choi, SG & Cho, SB 2018, Learning bayesian network to predict group emotion in kindergarten by evolutionary computation. in H Perez Garcia, J Alfonso-Cendon, L Sanchez Gonzalez, E Corchado & H Quintian (eds), International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings. Advances in Intelligent Systems and Computing, vol. 649, Springer Verlag, pp. 3-12, International Joint Conference on 12th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2017, 10th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2017 and 8th International Conference on European Transnational Education, ICEUTE 2017, Leon, Spain, 17/9/6. https://doi.org/10.1007/978-3-319-67180-2_1

Learning bayesian network to predict group emotion in kindergarten by evolutionary computation. / Choi, Seul Gi; Cho, Sung Bae.

International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings. ed. / Hilde Perez Garcia; Javier Alfonso-Cendon; Lidia Sanchez Gonzalez; Emilio Corchado; Hector Quintian. Springer Verlag, 2018. p. 3-12 (Advances in Intelligent Systems and Computing; Vol. 649).

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

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AU - Choi, Seul Gi

AU - Cho, Sung Bae

N1 - Funding Information: Acknowledgements. This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract (UD160066BD). Publisher Copyright: © 2018, Springer International Publishing AG.

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N2 - In the educational services, students’ emotions are an important factor that determine its effect. We have previously conducted research that led them to target emotions using environmental factors. However, the study used the bayesian network based on domain knowledge to predict emotions, which may differ from the actual environment. In this paper, we propose a method to learn the bayesian network for group emotion prediction in kindergarten from data through evolutionary computation. The learning data are brightness, color temperature, sound, volume, smell, temperature, humidity, and current emotion. The structure of the network is encoded with two chromosomes to represent nodes and arcs. To explore the optimal structure, evolutionary operators are used that can convey information in sets. We also experiment with various inference nodes not observed. Experimental results show that the accuracy is 85% with 20 inference nodes, which can replace network designed with domain knowledge. By comparing the evolution of the best model, we analyze the influential factors that determine the structure.

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Choi SG, Cho SB. Learning bayesian network to predict group emotion in kindergarten by evolutionary computation. In Perez Garcia H, Alfonso-Cendon J, Sanchez Gonzalez L, Corchado E, Quintian H, editors, International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings. Springer Verlag. 2018. p. 3-12. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-67180-2_1

Learning bayesian network to predict group emotion in kindergarten by evolutionary computation

Abstract

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Bibliographical note

All Science Journal Classification (ASJC) codes

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