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

Dados Bibliográficos

ID15469352
AutoresWen-Huai Hsieh (National Chung Cheng University), Dong‐Her Shih (0000-0003-1605-8488, National Yunlin University of Science and Technology, autor correspondente), Po-Yuan Shih (National Yunlin University of Science and Technology), Shih-Bin Lin (National Yunlin University of Science and Technology)
Ano2019
Volume16
Fascículo7
Páginas1233-1233
Data de publicação2019-04-06
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoInternational Journal of Environmental Research and Public Health (JOURNAL)
Identificadores do periódicoISSN: 1661-7827 • E-ISSN: 1660-4601
EditoraMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph16071233
PMID30959905
OpenAlexW2929737585
IdiomaEN
Referências citadas39

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

Addiction · Classifier (UML · Internet addiction disorder · Internet privacy · Internet users · Machine learning · Psychiatry · The Internet · World Wide Web · Computer Science · Network Security and Intrusion Detection · Psychology · Sentiment Analysis and Opinion Mining · Spam and Phishing Detection · Artificial Intelligence