The Use of Ensemble Classification and Clustering Methods of Machine Learning in the Study of Internet Addiction of Students

Oksana Klochko, Vasyl Fedorets, Vitalii Klochko, Maryna Kormer

Abstract

One of the relevant current vectors of study in machine learning is the analysis of the application peculiarities for methods of solving a specific problem. We will study this issue on the example of methods of solving the clustering and classification problem. Currently, we have a considerable number of machine learning algorithms – e.g. Expectation Maximization, Farthest First, K-Means, Expectation-Maximization, Hierarchical Clustering, Support vector machines, K-nearest neighbor, Logistic regression, Random Forest etc. – which can be used for clustering and classification. However, not all methods can be used for solving a specific task. The article describes the technology of empirical comparison of methods of clustering and classification problems solving using WEKA free software for machine learning. Empirical comparison of data clustering methods was based on the results of a survey conducted among students majoring in Computer Studies and dedicated to detecting signs of Internet Addiction (IA) (Internet Addiction is a behavioural disorder that occurs due to Internet misuse). As a continuation of the study of Internet Addiction of students, a survey of students of other specialties was conducted. Ensemble methods of machine learning classification were used to analyze these data. Empirical comparison of clustering algorithms (Expectation Maximization, Farthest First and K-Means) and ensemble classification algorithms (AdaBoost, Bagging, Random Forest and Vote) with the application of the WEKA machine learning system had the following results: it described the peculiarities of application of these methods in feature clustering and classification, the authors developed data instances’ clustering and classification models to detect signs of Internet addiction among students, the study concludes that these methods may be applicable to development of models detecting respondents with signs of IA related disorders and risk groups.

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Paper Citation


in Harvard Style

Klochko O., Fedorets V., Klochko V. and Kormer M. (2020). The Use of Ensemble Classification and Clustering Methods of Machine Learning in the Study of Internet Addiction of Students. In Proceedings of the 1st Symposium on Advances in Educational Technology - Volume 1: AET, ISBN 978-989-758-558-6, pages 241-260. DOI: 10.5220/0010923500003364


in Bibtex Style

@conference{aet20,
author={Oksana Klochko and Vasyl Fedorets and Vitalii Klochko and Maryna Kormer},
title={The Use of Ensemble Classification and Clustering Methods of Machine Learning in the Study of Internet Addiction of Students},
booktitle={Proceedings of the 1st Symposium on Advances in Educational Technology - Volume 1: AET,},
year={2020},
pages={241-260},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010923500003364},
isbn={978-989-758-558-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 1st Symposium on Advances in Educational Technology - Volume 1: AET,
TI - The Use of Ensemble Classification and Clustering Methods of Machine Learning in the Study of Internet Addiction of Students
SN - 978-989-758-558-6
AU - Klochko O.
AU - Fedorets V.
AU - Klochko V.
AU - Kormer M.
PY - 2020
SP - 241
EP - 260
DO - 10.5220/0010923500003364