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Effective Application of Naive Bayesian Classifier for Personal Online Learning Networks

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Abstract

Naive Bayesian classifier can be used to classify news and patients, but there are few studies on the classification of educational data. Based on Naïve Bayesian algorithm. This paper studies the relationship between course achievement and employment salary. Quantitative method is adopted as research methodology. The sample data sets were collected from Personal Online Learning Networks, which consist of the Student Performance Management System and Student Employment Management System. The sample category labels were constructed and the Hold-Out method was used to divide data sets into training sets and testing sets. 15 courses’ performance as feature vector and employment wage as category, if the attribute condition was independent, a Naïve Bayesian Classifier was established. The result indicating the higher the grade of DAWEB, ICT, INT and WNDW courses, the higher the employment wage. The conclusion is in accordance with the actual situation: Four courses mainly train students’ comprehensive practical ability. The students who have stronger practical abilities are highly demanded by employers, hence, the higher salary will be provided. At the end, regarding the class conditional probability of \(P(x_{i} = E|s = H)\) (Performance = E, salary = H) as the weight of courses, build a topological structure diagram of courses.

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References

  1. B. Inderpal and C. Edward, Advanced scout: data mining and knowledge discovery in NBA data, Data Mining and Knowledge Discovery, Vol. 1, pp. 121–125, 1997. https://doi.org/10.1023/a:1009782106822.

    Article  Google Scholar 

  2. S. V. Mehmet and G. Mustafa, Criminal prediction using Naive Bayesian theory, Neural Computing & Applications, Vol. 28, pp. 2581–2592, 2017. https://doi.org/10.1007/s00521-016-2205-z.

    Article  Google Scholar 

  3. A. Mohammad and C. Girija, Energy efficient data mining scheme for high dimensional data, Procedia Computer Science, Vol. 46, pp. 483–490, 2015. https://doi.org/10.1016/j.procs.2015.02.047.

    Article  Google Scholar 

  4. X. Li, L. M. Zhang and S. H. A. Zhang, Efficient Bayesian networks for slope safety evaluation with large quantity monitoring information, Geoscience Frontiers, Vol. 9, No. 6, pp. 1679–1687, 2018.

    Article  Google Scholar 

  5. M. Matthew, P. L. De Leon, and D. Keeley, Bayesian classification of falls risk, Gait & Posture, Vol. 67, pp. 99–103, 2019.

    Article  Google Scholar 

  6. S. R. Bhagya Shree and H. S. Sheshadri, Diagnosis of Alzheimer’s disease using Naive Bayesian Classifier, Neural Computing & Applications, Vol. 29, pp. 123–132, 2018. https://doi.org/10.1007/s00521-016-2416-3.

    Article  Google Scholar 

  7. R. Cristobal and V. Sebastian, Educational data mining: a survey from 1995 to 2005, Expert Systems with Applications, Vol. 33, No. 1, pp. 135–146, 2007. https://doi.org/10.1016/j.eswa.2006.04.005.

    Article  Google Scholar 

  8. Y. Zhu, A data driven education decision support system, International Journal of Emerging Technologies in Learning (iJET), Vol. 13, No. 11, p. 4, 2018. https://doi.org/10.3991/ijet.v13i11.9582.

    Article  Google Scholar 

  9. F. Grivokostopoulou, I. Perikos, and I. Hatzilygeroudis, Utilizing semantic web technologies and data mining techniques to analyze students learning and predict final performance, International Conference on Teaching, Assessment and Learning (TALE), pp 488–494, 2014. https://doi.org/10.1109/tale.2014.7062571.

  10. I. E. Livieris, T. Mikropoulos, and P. Pintelas, A decision support system for predicting students’ performance, Themes in Science and Technology Education, Vol. 9, pp. 43–57, 2016.

    Google Scholar 

  11. H. M. Nagy, W. M. Aly, and O. F. Hegazy, An educational data mining system for advising higher education students, World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, Vol. 7, No. 10, pp. 175–179, 2013.

    Google Scholar 

  12. A. Y. Noaman, J. M. Luna, A. H. M. Ragab, and S. Ventura, Recommending degree studies according to students’ attitudes in high school by means of subgroup discovery, International Journal of Computational Intelligence Systems, Vol. 9, No. 6, pp. 1101–1117, 2016. https://doi.org/10.1080/18756891.2016.1256573.

    Article  Google Scholar 

  13. Y. Lang and L. Kong, The U.S. Government Released “Big Data Research and Development Initiative”, 2012. http://escj.cnic.cn/cn/y2012/v3/i2/89.

  14. S. Patil and S. Kulkarni, Mining social media data for understanding students’ learning experiences using Memetic algorithm, Materials Today: Proceedings, Vol. 5, No. 1, pp. 693–699, 2015. https://doi.org/10.1016/j.matpr.2017.11.135.

    Google Scholar 

  15. C. Vialardi, J. Chue, and J. P. Peche, A data mining approach to guide students through the enrollment process based on academic performance, User Modeling and User-Adapted Interaction, Vol. 21, pp. 217–248, 2011. https://doi.org/10.1007/s11257-011-9098-4.

    Article  Google Scholar 

  16. I. E. Livieris, T. Kotsilieris, V. Tampakas, and P. Pintelas, Improving the evaluation process of students’ performance utilizing a decision support software, Neural Computing and Applications, Vol. 15, pp. 235–246, 2018. https://doi.org/10.1007/s00521-018-3756-y.

    Google Scholar 

  17. M. Koutina and K. L. Kermanidis, Predicting postgraduate students’ performance using machine learning techniques, Artificial Intelligence Applications and Innovations, Vol. 364, pp. 159–168, 2011. https://doi.org/10.1007/978-3-642-23960-1_20.

    Article  Google Scholar 

  18. Z. H. Zhou, Machine Learning. Tsinghua University, Beijing, 2016. (Chapter 2,3,7).

    Google Scholar 

  19. A. Biedermanna, S. Bozzaba, and F. Taronia, Analysing and exemplifying forensic conclusion criteria in terms of Bayesian decision theory, Science & Justice, Vol. 58, No. 2, pp. 159–165, 2018. https://doi.org/10.1016/j.scijus.2017.07.002.

    Article  Google Scholar 

  20. A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the American Statistical Association, Vol. 100, No. 469, pp. 1–5, 1977.

    MathSciNet  MATH  Google Scholar 

  21. N. Marhamati, E. K. Buxton, and S. Rahimi, Integration of Z-numbers and Bayesian decision theory: a hybrid approach to decision making under uncertainty and imprecision, Applied Soft Computing, Vol. 72, pp. 273–290, 2018. https://doi.org/10.1016/j.asoc.2018.07.053.

    Article  Google Scholar 

  22. A. alias Balamurugan, R. Rajaram, S. Pramala, S. Rajalakshmi, C. Jeyendran, and J. D. Prakash, NB+: an improved Naïve Bayesian algorithm, Knowledge-Based Systems, Vol. 24, No. 5, pp. 563–569, 2011. https://doi.org/10.1016/j.knosys.2010.09.007.

    Article  Google Scholar 

  23. M. Fanuel and J.A.K. Suykens, Deformed Laplacians and spectral ranking in directed networks, Applied and Computational Harmonic Analysis, 2017. https://doi.org/10.1016/j.acha.2017.09.002. (in press).

  24. L. C. Lee, C. Y. Liong, and A. A. Jemain, Validity of the best practice in splitting data for hold-out validation strategy as performed on the ink strokes in the context of forensic science, Microchemical Journal, Vol. 139, pp. 125–133, 2018. https://doi.org/10.1016/j.microc.2018.02.009.

    Article  Google Scholar 

  25. G. Jiang and W. Wang, Error estimation based on variance analysis of k-fold cross-validation, Pattern Recognition, Vol. 69, pp. 94–106, 2017. https://doi.org/10.1016/j.patcog.2017.03.025.

    Article  Google Scholar 

  26. W. Marciszewski and G. Plebanek, On measures on Rosenthal compacta, Journal of Mathematical Analysis and Applications, Vol. 385, No. 1, pp. 185–193, 2012. https://doi.org/10.1016/j.jmaa.2011.06.030.

    Article  MathSciNet  MATH  Google Scholar 

  27. X.-L. Hu, An extension of Rosenthal’s inequality, Applied Mathematics and Computation, Vol. 2018, No. 8, pp. 4638–4640, 2011. https://doi.org/10.1016/j.amc.2011.09.001.

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

Our research is supported by the Project supported by the Educational Reform of Higher Education in Jiangsu Province of China (2017JSJG283).

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Authors and Affiliations

  1. Management & Science University, Shah Alam, Malaysia

    Deyan Wang & Amritl Jaharadak Adam

  2. Wuxi Institute of Technology, School of Internet of Things Technology, Wuxi, Jiangsu, China

    Deyan Wang, Ying Xiao & Ya Gao

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  1. Deyan Wang
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Correspondence to Deyan Wang.

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Wang, D., Adam, A.J., Xiao, Y. et al. Effective Application of Naive Bayesian Classifier for Personal Online Learning Networks. Int J Wireless Inf Networks 26, 174–182 (2019). https://doi.org/10.1007/s10776-019-00436-9

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