Classification and Prediction of Student Academic Performance using Gray Wolf Optimization Based Relief-F Budget Random Forest
Kongara Deepika1, Nallamothu Sathyanarayana2
1Kongara Deepika, CSE, Talla Padmavathi College of Engineering, Telangana, India.
2Dr. Nallamothu Sathyanarayana, CSE, Nagole Institute of Technology and Sciences, Telangana, India.
Manuscript received on 12 August 2019. | Revised Manuscript received on 21 August 2019. | Manuscript published on 30 September 2019. | PP: 4411-4418 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5534098319/2019©BEIESP | DOI: 10.35940/ijrte.C5534.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The student academic prediction model helps to predict the student performance that helps the university to provide necessary care to the particular students. Efficient prediction model helps to encourage the student for better performance in the academic. In this research, the Relief-F Budget Tree Random Forest with Gray Wolf Optimization (RFBTRF-GWO) method is proposed for the feature selection. The Gray Wolf Optimization (GWO) helps to scale the relevant feature with ranking order from the features selected by the Relief-F Budget Tree Random Forest (RFBTRF). The selected features are given as input to the classifier for the effective prediction. The k-Nearest Neighbor (kNN) and Artificial Neural Network (ANN) are used for the classification. The proposed RFBTRF-GWO method is evaluated on the three datasets such as two UCI datasets and one collected dataset. The RFBTRF-GWO has a higher performance accuracy of 96.2 % while the existing method RFBTRF has an accuracy of 70.88 %.
Index Terms: Artificial Neural Network, Gray Wolf Optimization Based Relief-F Budget Random Forest, k-Nearest Neighbor, and Student Academic Prediction.
Scope of the Article: Classification