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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.9 No.4
- 발행연도
- 2020.8
- 수록면
- 298 - 311 (14page)
- DOI
- 10.5573/IEIESPC.2020.9.4.298
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초록· 키워드
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Deep learning has recently attracted increasing interest for several applications, and great progress has been made. This paper introduces a novel application of a deep learning framework called deep belief networks (DBNs) that can be used to predict the predicting academic performance of larger datasets. First, unsupervised training is considered a so-called pre-training section. The stacked restricted Boltzmann machine (RBM) was trained to obtain the trained weights instead of random initial weights. Subsequently, supervised learning was adopted using backpropagation in the fine-tuning section to classify the student performance levels. An optimization approach for improving the classification performance of the proposed DBN was proposed. The optimization approach consisted of using a feature selection method to obtain the optimal feature subset, optimizing the DBN model with the optimal values of the hyperparameters, and applying the L2-regularization method for weight decay. The experiment was carried out with two phases. Phase1 was implemented with an actual dataset. Phase 2 was then implemented with four artificial datasets of increasing sizes. Many experiments were performed independently on each dataset. With a larger dataset, the improved DBN generated the highest accuracy and lowest root mean square error with more accuracy and effectiveness than the other proposed algorithms.
목차
Abstract
1. Introduction
2. Related Work
3. The Proposed Research Methods
4. Data Description and Preprocessing
5. Performance Evaluation Metrics
6. Optimization Approach for DBN Model
7. Experimental Result and Discussion
8. Summary and Conclusion
References
참고문헌 (31)
참고문헌 신청
Kumar J / 2015 / A Comparative Study of Educational Data Mining / International Journal of Electrical Electronics & Computer Science Engineering (Special)
Goodfellow I. / 2016 / Deep Learning / MIT Press
Mazdak F / 2016 / Towards a Spiking Deep Belief Network for Face Recognition Application / The 6th International Conference on Computer and Knowledge Engineering (ICCKE 2016)
Syed F. Q / 2019 / Automatic Deep Feature learning via Pathch-Based Deep Belief Network for Vertebrae Segmentation in CT Image / Applied Science 9(69)
Zhou S. / 2018 / Active Deep Networks for Semi-Supervised Sentiment Classification / Proceedings of 23rd International Conference on Computation Linguistics: Posters, Association for Computational Linguistics :115~1528
Goodfellow I. / 2016 / Deep Learning / MIT Press
Mazdak F / 2016 / Towards a Spiking Deep Belief Network for Face Recognition Application / The 6th International Conference on Computer and Knowledge Engineering (ICCKE 2016)
Syed F. Q / 2019 / Automatic Deep Feature learning via Pathch-Based Deep Belief Network for Vertebrae Segmentation in CT Image / Applied Science 9(69)
Zhou S. / 2018 / Active Deep Networks for Semi-Supervised Sentiment Classification / Proceedings of 23rd International Conference on Computation Linguistics: Posters, Association for Computational Linguistics :115~1528
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