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

Wen-Huai Hsieh, Dong-Her Shih, Po-Yuan Shih and Shih-Bin Lin
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Wen-Huai Hsieh: Department of Information Management, National Chung Cheng University; Director of Chang-Hua Hospital, Chang-Hua County 51341, Taiwan
Dong-Her Shih: Department of Information Management, National Yunlin University of Science & Technology, Douliu 64002, Taiwan
Po-Yuan Shih: Department of Finance, National Yunlin University of Science & Technology, Douliu 64002, Taiwan
Shih-Bin Lin: Department of Information Management, National Yunlin University of Science & Technology, Douliu 64002, Taiwan

IJERPH, 2019, vol. 16, issue 7, 1-17

Abstract: 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: internet addiction; ensemble classifier; case-based reasoning; machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
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