Predicted Increase Enrollment in Higher EducationUsing Neural Networks and Data Mining Techniques

Advanced data mining techniques can be used in universities classification,discovering specific patterns in the determination of successful students, design of aplan or a teaching method and finding critical points of financial management. Inthis article, we proposed a method to predict the rate of student enrollment incoming years. The data for this research were from data sets of volunteers’postgraduate Islamic Azad university entrance exam. At first stage, we built 15different neural networks. In order to increase the accuracy, we employed thecollective bagging and boosting models. Finally, the four models, neural networks,decision trees, Bayes simple and logistic regression, were applied on the dataset andevaluate by three criteria included, accuracy, Matthews correlation and ROC curve.The findings indicated that to predict Students who were accepted would enroll;the bagging method is the most accurate one

Keywords:

Bagging Model , Boosting , Model , Decision Tree , Bayesian Simple , Kappa Precision , Mathews Correlation , T-Test Curve