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.
Keywords: case-based reasoning; ensemble classifier; internet addiction; machine learning.