Predicted Increase Enrollment in Higher Education Using Neural Networks and Data Mining Techniques

Subject Areas : B. Computer Systems Organization

Behzad Nakhkob ¹ , Maryam Khademi ²

1 - Student of Department of Computer Science, South Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Assistant Professor Department of Applied Mathematics, South Tehran Branch, Islamic Azad University, Tehran, Iran

Received: 2016-10-02 Accepted : 2016-10-02 Published : 2016-11-01

Keywords: model, Decision tree, Bagging model, Boosting, Bayesian Simple, Kappa Precision, Mathews Correlation, T-Test Curve,

Abstract :

Advanced data mining techniques can be used in universities classification, discovering specific patterns in the determination of successful students, design of a plan or a teaching method and finding critical points of financial management. In this article, we proposed a method to predict the rate of student enrollment in coming years. The data for this research were from data sets of volunteers’ postgraduate Islamic Azad university entrance exam. At first stage, we built 15 different neural networks. In order to increase the accuracy, we employed the collective bagging and boosting models. Finally, the four models, neural networks, decision trees, Bayes simple and logistic regression, were applied on the dataset and evaluate 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.