Application of knowledge based decision technique to predict student enrollment decision

In this paper we are proposing a new Attribute Selection Measure Function (heuristic) on existing C4.5 algorithm. The advantage of heuristic is that the split information never approaches zero, hence produces stable Rule set and Decision Tree. In ideal situation, in admission process in engineering colleges in India, a student takes admission based on AIEEE rank and family pressure. If the student does not get admission in the desired branch of engineering, then they find it difficult to take decision which will be the suitable branch. The proposed knowledge based decision technique will guide the student for admission in proper branch of engineering. Another approach is also made to analyze the accuracy rate for decision tree algorithm (C5.0) and back propagation algorithm (ANN) to find out which one is more accurate for decision making. In this research work we have used the AIEEE2007 Database.