Multidimensional Approach Based on Deep Learning to Improve the Prediction Performance of DNN Models
DOI:
https://doi.org/10.3991/ijet.v14i02.8873Keywords:
Educational Data Mining (EDM), Classification, Deep Neural Network (DNN), Deep Learning, Principal Component Analysis (PCA)Abstract
The most of collected data samples from E-learning systems consist of correlated information caused by overlapping input instances, which decrease the classifier credibility and reliability. This paper presents an improved classification model based on Deep Learning and Principal Component Analysis (PCA) method as its use in reducing the dimensionality of data. By this task, we introduce the best learning process to extract just the useful parameters that describe students’ per-formances in an E-learning system. One of the primary goals of this technique is to help earlier in detecting the dropouts and discovering of students who need special attention, so that the teachers could provide the appropriate counseling at the right time. This study presents the proposal approach and its algorithms. In addition, it shows how deep neural network was modeled in the training phase, and how PCA helps in the elimination of correlated information in our dataset to increase the classifier performance. Finally, we introduce an example of an appli-cation of the method in a data mining scenario, find out more references for fur-ther information.
Downloads
Published
How to Cite
Issue
Section
License
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution Licence (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
By submitting an article the author grants to this journal the non-exclusive right to publish it. The author retains the copyright and the publishing rights for his article without any restrictions.