Application of Hybrid Sampling and Stacked Deep Networks: Predicting the AI Convergence Education using Education Survey Data

Application of Hybrid Sampling and Stacked Deep Networks: Predicting the AI Convergence Education using Education Survey Data
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-10
Year of Publication : 2022
Authors : Haewon Byeon
DOI : 10.14445/22315381/IJETT-V70I10P240

How to Cite?

Haewon Byeon, "Application of Hybrid Sampling and Stacked Deep Networks: Predicting the AI Convergence Education using Education Survey Data," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 408-414, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I10P240

Abstract
This study proposed a method for predicting educational satisfaction based on hybrid sampling and stacked deep networks (SDN). We compared the performance of logistic regression (LR), support vector machine (SVM), decision tree (DT), and random forest (RF) machine learning algorithms with that of the SDN proposed in this study. The SDN method based on hybrid sampling proposed in this study had superior accuracy, recall, and precision performance to other ML algorithms. Especially when hybrid sampling was applied to the SDN algorithm, recall, and precision performance was greatly improved. Since SND has a feature extraction function and can be applied to unrefined data, additional studies should evaluate the performance by applying the proposed model to unrefined data with various attributes.

Keywords
Stacked deep networks, Logistic regression, Support vector machine, Hybrid sampling, Decision tree.

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