Ensemble Methods to Predict the Locality Scope of Indian and Hungarian Students for the Real Time: Preliminary Results

In the present study, we presented ensemble classifier to predict the locality scope (National or International) of the student based on their motherland and sex toward Information and Communication Technology (ICT) and Mobile Technology (MT). For this, a primary dataset of 331 samples from Indian and Hungarian university was gathered during the academic year 2017–2018. The dataset contained 331 instances and 37 features which belonged to the four major ICT parameters attitude, development and availability, educational benefits and usability of modern ICT resources, and mobile technology in higher education. In addition to class balancing with Synthetic Minority Over-Sampling Technique (SMOTE), Adaptive Boosting (AdboostM1) and bagging ensemble technique is applied with Artificial Neural Network (ANN) and Random Forest (RF) classifiers in Weka tool. Findings of the study infer that the ANN achieved higher accuracy (92.94%) as compared to RF’s accuracy (92.25%). The author’s contribution is to apply ensemble methods with standard classifiers to provide more accurate and consistent results. On the one hand, with the use of bagging, the ANN achieved 92.94% accuracy, and on the other hand, AdboostM1 has also significantly improved the prediction accuracy and RF provided 92.25% accuracy. Further, the statistical T-test at the 0.05 significance level proved no significant difference between the accuracy of RF and ANN classifier to predict the locality scope of the student. Also, the authors found a significant difference between the CPU prediction time between bagging with ANN and AdboostM1 with RF.