文章基本信息

标题：Optimize the Combination of Categorical Variable Encoding and Deep Learning Technique for the Problem of Prediction of Vietnamese Student Academic Performance
其他标题：Optimize the Combination of Categorical Variable Encoding and Deep Learning Technique for the Problem of Prediction of Vietnamese Student Academic Performance
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作者：Do Thi Thu Hien ; Cu Thi Thu Thuy ; Tran Kim Anh 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：11
DOI：10.14569/IJACSA.2020.0111135
出版社：Science and Information Society (SAI)
摘要：Deep learning techniques have been successfully applied in many technical fields such as computer vision and natural language processing, and recently researchers have paid much attention to the application of this technology in socio-economic problems including the student academic performance prediction (SAPP) problem. In this specialization, this study focusses on both designing an appropriate Deep learning model and handling categorical input variables. In fact, categorical data variables are quite popular in student academic performance prediction problem, and deep learning technique in particular or artificial neural network in general only work well with numerical data variables. Therefore, this study investigates the performance of the combination categorical encoding methods including label encoding, one-hot encoding and “learned” embedding encoding with deep learning techniques including Deep Dense neural network and Long short-term memory neural network for SAPP problem. In experiment, this study compared these proposed models with each other and with some prediction methods based on other machine learning algorithms at the same time. The results showed that the categorical data transformation method using the “learned” embedding encoding improved performance of the deep learning models, and its combination with long short-term memory network gave an outstanding result for the researched problem.
关键词：Deep learning techinique; categorical data type; “learned” embedding encoding; student academic performance prediction