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Predicting Grade of General Entrance Exam Using Machine Learning Techniques

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Advances in Intelligent Information Hiding and Multimedia Signal Processing

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 212))

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Abstract

Goal of research work is to determine the most important features on predicting the grade of the General Entrance Exam (GEE). The features are high school student’s grade, personal information, and state exam results. We collected 96,827 high school students data and compared the F1 measures with different classification techniques such as decision tree, logistic regression, artificial neural network, and support vector machine. Among these techniques, the SVM provided the best F1 measure that is 0.70.

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Correspondence to Zoljargal Munkhjargal .

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Bayanmunkh, N., Munkhjargal, Z., Ganbold, A. (2021). Predicting Grade of General Entrance Exam Using Machine Learning Techniques. In: Pan, JS., Li, J., Ryu, K.H., Meng, Z., Klasnja-Milicevic, A. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 212. Springer, Singapore. https://doi.org/10.1007/978-981-33-6757-9_18

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