[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.22937/IJCSNS.2021.21.8.5

Algorithms for Classifying the Results at the Baccalaureate Exam-Comparative Analysis of Performances

Marcu, Daniela (Stefan cel MareUniversity of Suceava)
Danubianu, Mirela (Stefan cel MareUniversity of Suceava)
Barila, Adina (Stefan cel MareUniversity of Suceava)
Simionescu, Corina (Stefan cel MareUniversity of Suceava)

Publication Information

International Journal of Computer Science & Network Security / v.21, no.8, 2021 , pp. 35-42 More about this Journal

Abstract

In the current context of digitalization of education, the use of modern methods and techniques of data analysis and processing in order to improve students' school results has a very important role. In our paper, we aimed to perform a comparative study of the classification performances of AdaBoost, SVM, Naive Bayes, Neural Network and kNN algorithms to classify the results obtained at the Baccalaureate by students from a college in Suceava, during 2012-2019. To evaluate the results we used the metrics: AUC, CA, F₁, Precision and Recall. The AdaBoost algorithm achieves incredible performance for classifying the results into two categories: promoted / rejected. Next in terms of performance is Naive Bayes with a score of 0.999 for the AUC metric. The Neural Network and kNN algorithms obtain scores of 0.998 and 0.996 for AUC, respectively. SVM shows poorer performance with the score 0.987 for AUC. With the help of the HeatMap and DataTable visualization tools we identified possible correlations between classification results and some characteristics of data.

Keywords

Classification algorithms; data visualization;

Citations & Related Records

Reference

1	Wosiak, A., Zamecznik A. and K. Niewiadomska-Jarosik, "Supervised and unsupervised machine learning for improved identification of intrauterine growth restriction types," 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), 2016, pp. 323-329.
2	Singh, A., Thakur, N. and A. Sharma, "A review of supervised machine learning algorithms," 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), 2016, pp. 1310-1315.
3	Chiru, C., Trausan-Matu, S, Rebedea, T. O imbunatatire a performantelor algoritmului KNN in sistemele de recomandare pe web. In: Buraga, S.C., Juvina, I. (Eds.) Interactiune Om-Calculator 2008. ISSN 1843-4460, (Conferinta Nationala de Interactiune Om-Calculator, Iasi 18-19 Septembrie 2008), Editura MatrixROM Bucuresti, pp.41-48.
4	Hu, L.Y., Huang, MW., Ke, SW. et al. The distance function effect on k-nearest neighbor classification for medical datasets. SpringerPlus 5, 1304 (2016). https://doi.org/10.1186/s40064-016-2941-7. DOI
5	Marcu, D., Danubianu M., Simionescu C. (2021) Comparative analysis of predictve models on online education in context of covid-19 - A case study, INTED2021 Proceedings, pp. 4403-4412.
6	Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65 6, 386-408. DOI
7	Wang, Ruihu. (2012). AdaBoost for Feature Selection, Classification and Its Relation with SVM, A Review. Physics Procedia. 25.800-807. 10.1016/j.phpro.2012.03.160. DOI
8	Jiawei Han, Micheline Kamber, Jian Pei, 2 - Getting to Know Your Data, Editor(s): Jiawei Han, Micheline Kamber, Jian Pei, In The Morgan Kaufmann Series in Data Management Systems, Data Mining (Third Edition), Morgan Kaufmann, 2012, Pages 39-82, ISBN 9780123814791, https://doi.org/10.1016/B978-0-12-381479-1.00002-2.
9	Zhang, Harry. (2004). The Optimality of Naive Bayes. Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2004. 2.
10	Scikit-learn 0.24.2. Available from, https://scikit-learn.org/stable/modules/naive_bayes.html.
11	Vrejoiu, Mihnea. (2019). Retele neuronale convolutionale, Big Data si Deep Learning in analiza automata de imagini. Revista Romana de Informatica si Automatica. 29. 91-114. 10.33436/v29i1y201909. DOI
12	Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (2016). "6.5 Back-Propagation and Other Differentiation Algorithms". Deep Learning. MIT Press. pp. 200-220. ISBN 9780262035613.
13	Dobrea, Dan-Marius. Curs "Tehnici de inteligenta cpmputationala. Aplicatii in electronica si biomedicina", capitolul "Retele neuronale artificiale". Universitatea Tehnica "Gheorghe Asachi" Iasi, anul IV, Facultatea de Electronica, Telecomunicatii si Tehnologia Informatiei.