Next Article in Journal
Utilization of a Mobile Dental Vehicle for Oral Healthcare in Rural Areas
Next Article in Special Issue
The Korea Cancer Big Data Platform (K-CBP) for Cancer Research
Previous Article in Journal
The Role of Motor Learning on Measures of Physical Requirements and Motor Variability During Repetitive Screwing
Open AccessArticle

An Ensemble Classifier with Case-Based Reasoning System for Identifying Internet Addiction

1
Department of Information Management, National Chung Cheng University; Director of Chang-Hua Hospital, Chang-Hua County 51341, Taiwan
2
Department of Information Management, National Yunlin University of Science & Technology, Douliu 64002, Taiwan
3
Department of Finance, National Yunlin University of Science & Technology, Douliu 64002, Taiwan
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(7), 1233; https://doi.org/10.3390/ijerph16071233
Received: 9 February 2019 / Revised: 29 March 2019 / Accepted: 4 April 2019 / Published: 6 April 2019
Internet usage has increased dramatically in recent decades. With this growing usage trend, the negative impacts of Internet usage have also increased significantly. One recurring concern involves users with Internet addiction, whose Internet usage has become excessive and disrupted their lives. In order to detect users with Internet addiction and disabuse their inappropriate behavior early, a secure Web service-based EMBAR (ensemble classifier with case-based reasoning) system is proposed in this study. The EMBAR system monitors users in the background and can be used for Internet usage monitoring in the future. Empirical results demonstrate that our proposed ensemble classifier with case-based reasoning (CBR) in the proposed EMBAR system for identifying users with potential Internet addiction offers better performance than other classifiers. View Full-Text
Keywords: internet addiction; ensemble classifier; case-based reasoning; machine learning internet addiction; ensemble classifier; case-based reasoning; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Hsieh, W.-H.; Shih, D.-H.; Shih, P.-Y.; Lin, S.-B. An Ensemble Classifier with Case-Based Reasoning System for Identifying Internet Addiction. Int. J. Environ. Res. Public Health 2019, 16, 1233.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop