Browse > Article
http://dx.doi.org/10.7465/jkdi.2013.24.1.13

Self-diagnostic system for smartphone addiction using multiclass SVM  

Pi, Su Young (Institute of Liberal Education, Catholic University of Daegu)
Publication Information
Journal of the Korean Data and Information Science Society / v.24, no.1, 2013 , pp. 13-22 More about this Journal
Abstract
Smartphone addiction has become more serious than internet addiction since people can download and run numerous applications with smartphones even without internet connection. However, smartphone addiction is not sufficiently dealt with in current studies. The S-scale method developed by Korea National Information Society Agency involves so many questions that respondents are likely to avoid the diagnosis itself. Moreover, since S-scale is determined by the total score of responded items without taking into account of demographic variables, it is difficult to get an accurate result. Therefore, in this paper, we have extracted important factors from all data, which affect smartphone addiction, including demographic variables. Then we classified the selected items with a neural network. The result of a comparative analysis with backpropagation learning algorithm and multiclass support vector machine shows that learning rate is slightly higher in multiclass SVM. Since multiclass SVM suggested in this paper is highly adaptable to rapid changes of data, we expect that it will lead to a more accurate self-diagnosis of smartphone addiction.
Keywords
Backpropagation algorithm; Gaussian kernel; multiclass support vector machine; S-scale;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Bharat, A. and Barin, N. (1997). Performance evaluation of neural network decision models. Journal of Management Information Systems, 14, 201-230.   DOI
2 Cheng, J., Yang, S. and Lu, S. (2007). Virus detection and alert for smartphones. Proceedings of the Fifth International Conference on Mobile System, 258-271.
3 Gjorgii, M., Dejan, G. and Ivan, C. (2009). A multi-class SVM classifier utilizing binary detection tree. Informetica, 33, 233-241.
4 Hwang, H. S. and Sohn, S. H. (2011). Exploring factors affecting smartphone addiction-characteristics of users and functional attributes. Korean Journal of Broadcasting and Telecommunication Studies, 25, 277-313.
5 Choi, H. S., Lee, H. K. and Ha, J. (2012). The influence of smartphone addiction on mental health, campus life and personal relations. Journal of the Korean Data & Information Science Society, 23, 1005-1015.   DOI   ScienceOn
6 Ko, J. P. (2005). Solving multi-class problem using support vector machines. Journal of Korean Institute of Information Scientists and Engineers, 12, 1260-1270.
7 Kwon, D. S. and Kim, J. H. (2011). An empirical study applying the self-determination factors to flow and satisfaction of smartphone. Journal of the Society for e-Business Studies, 16, 197-214.   DOI   ScienceOn
8 Lee, Y. I. (2010). A study on the smart-phone TAM and satisfaction of college students. Journal of Korea Research Academy of Distribution and Management, 13, 93-101.   DOI
9 Mercer, J. (1909). Functions of positive and negative type and their connection with the theory of integral equations. Philosophical Transactions of the Royal Society A, 415-446.
10 National Information Society Agency. (2012). Diagnosing smartphone addiction scale, Korean Internet Addiction Center, Seoul.
11 Park, J. Y. and Leem, C. H. (2003). Support vector learning for abnormality detection problems. Journal of Korean Institute of Intelligent Systems, 13, 266-274.   DOI
12 Pi, S. Y., Park, H. J. and Ryu, K. H. (2011). An analysis of satisfaction index on computer education of university using kernal machine. Journal of the Korean Data & Information Science Society, 22, 921-929.
13 Rho, M. J. and Kim, J. H. (2010). An exploratory study on smart-phone and service convergence. Journal of the Society for e-Business Studies, 15, 59-77.