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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Milan Ranđelović & Aleksandar Aleksić & Radovan Radovanović & Vladica Stojanović & Milan Čabarkapa & Dragan Ranđelović, 2022. "One Aggregated Approach in Multidisciplinary Based Modeling to Predict Further Students’ Education," Mathematics, MDPI, vol. 10(14), pages 1-23, July.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2381-:d:857228
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    References listed on IDEAS

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    3. Vaidya, Omkarprasad S. & Kumar, Sushil, 2006. "Analytic hierarchy process: An overview of applications," European Journal of Operational Research, Elsevier, vol. 169(1), pages 1-29, February.
    4. Rothstein, Jesse M, 2004. "College performance predictions and the SAT," Department of Economics, Working Paper Series qt59s4j4m4, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    5. Diego Buenaño-Fernández & David Gil & Sergio Luján-Mora, 2019. "Application of Machine Learning in Predicting Performance for Computer Engineering Students: A Case Study," Sustainability, MDPI, vol. 11(10), pages 1-18, May.
    6. Rothstein, J.M.Jesse M., 2004. "College performance predictions and the SAT," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 297-317.
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