Study on Factors of College Student's Network Service Time Based on Bayesian Networks

Article Preview

Abstract:

Due to the uncertainty of the factors that influence the network service time and other characters of college student, Bayesian Network is used to model this kind of system. Different algorithms are used for learning Bayesian Networks in order to compare several models. It is suggested that researchers can use Bayesian Networks to explore the potential relationship between variables of complex social problems. The result indicates that learning target and family closeness degree are the key variables which influenced college students network service time. Origin of student and family economy didnt influence college students network service time directly. Schools and community should strengthen the education of college students life planning and communication with parents.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

996-1000

Citation:

Online since:

January 2014

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Young KS. Internet Addiction: The Emergence of a New Clinical Disorder Paper Presented at the 104th Annual Meeting of the American Psychological Association, Toronto, 19961.

Google Scholar

[2] JENSEN F V. Bayesian Networks and Decision Graphs [M]. Springer, New York, (2001).

Google Scholar

[3] Lin Shimin, et al. Construction of the Bayesian Network and its Application In Data Mining [J]. Journal of Tsinghua University (Natural Science Edition), 2001, 41(1): 49~52.

Google Scholar

[4] Daly R,Shen Q. Methods to Accelerate the Learning of Bayesian Network Structures. In Proceedings of the 2007 UK Workshop on Computational Intelligence, Imperial College, London. (2007).

Google Scholar

[5] Tsamardinos I, Brown LE,Aliferis CF. The Max-Min Hill-Climbing Bayesian Network Structure Learning Algorithm. Machine Learning, 2006, 65 (1): 31~78.

DOI: 10.1007/s10994-006-6889-7

Google Scholar

[6] Tsamardinos I, Aliferis CF, Statnikov A. Algorithms for Large Scale Markov Blanket Discovery. In Proceedings of the Sixteenth International Florida Artificial Intelligence Research Society Conference, AAAI Press. 2003: 376~381.

Google Scholar

[7] Yaramakala S, Margaritis D. Speculative Markov Blanket Discovery for Optimal Feature Selection. In ICDM'05: Proceedings of the Fifth IEEE International Conference on Data Mining,. IEEE Computer Society, Washington, DC, USA. 2005: 809~812.

DOI: 10.1109/icdm.2005.134

Google Scholar

[8] Cooper GF,Herskovits E. A Bayesian Method for the Induction of Probabilistic Networks from Data. Machine Learning, 1992, 9 (4): 309~347.

DOI: 10.1007/bf00994110

Google Scholar