Open AccessClassification and Prediction of Student Academic Performance using Gray Wolf Optimization Based Relief-F Budget Random Forest
Author(s) -
Kongara Deepika*,
Nallamothu Sathyanarayana
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c5534.098319
Subject(s) - random forest , artificial intelligence , computer science , gray (unit) , machine learning , classifier (uml) , feature selection , ranking (information retrieval) , artificial neural network , gray wolf , data mining , pattern recognition (psychology) , medicine , paleontology , biology , canis , radiology
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 %.