One Aggregated Approach in Multidisciplinary Based Modeling to Predict Further Students’ Education

Milan Ranđelović, Aleksandar Aleksić, Radovan Radovanović, Vladica Stojanović, Milan Čabarkapa and Dragan Ranđelović
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Milan Ranđelović: Science Technology Park, 18000 Niš, Serbia
Aleksandar Aleksić: Faculty of Diplomacy and Security, University Union-Nikola Tesla Belgrade, 11000 Beograd, Serbia
Radovan Radovanović: Department of Forensic Engineering, University of Criminal Investigation and Police Studies, 11000 Beograd, Serbia
Vladica Stojanović: Department of Information Technology, University of Criminal Investigation and Police Studies, 11000 Beograd, Serbia
Milan Čabarkapa: Faculty of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia
Dragan Ranđelović: Faculty of Diplomacy and Security, University Union-Nikola Tesla Belgrade, 11000 Beograd, Serbia

Mathematics, 2022, vol. 10, issue 14, 1-23

Abstract: In this paper, one multidisciplinary-applicable aggregated model has been proposed and verified. This model uses traditional techniques, on the one hand, and algorithms of machine learning as modern techniques, on the other hand, throughout the determination process of the relevance of model attributes for solving any problems of multicriteria decision. The main goal of this model is to take advantage of both approaches and lead to better results than when the techniques are used alone. In addition, the proposed model uses feature selection methodology to reduce the number of attributes, thus increasing the accuracy of the model. We have used the traditional method of regression analysis combined with the well-known mathematical method Analytic Hierarchy Process (AHP). This approach has been combined with the application of the ReliefF classificatory modern ranking method of machine learning. Last but not least, the decision tree classifier J48 has been used for aggregation purposes. Information on grades of the first-year graduate students at the Criminalistics and Police University, Belgrade, after they chose and finished one of the three possible study modules, was used for the evaluation of the proposed model. To the best knowledge of the authors, this work is the first work when considering mining closed frequent trees in case of the streaming of time-varying data.

Keywords: theory of mathematical modeling; applied mathematics; classification and discrimination; multicriteria decision making; linear regression; prediction theory (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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