Role of FCBF Feature Selection in Educational Data Mining
The Educational Data Mining (EDM) is a very vigorous area of Data Mining (DM), and it is helpful in predicting the performance of students. Student performance prediction is not only important for the student but also helpful for academic organization to detect the causes of success and failures of students. Furthermore, the features selected through the students’ performance prediction models helps in developing action plans for academic welfare. Feature selection can increase the prediction accuracy of the prediction model. In student performance prediction model, where every feature is very important, as a neglection of any important feature can cause the wrong development of academic action plans. Moreover, the feature selection is a very important step in the development of student performance prediction models. There are different types of feature selection algorithms. In this paper, Fast Correlation-Based Filter (FCBF) is selected as a feature selection algorithm. This paper is a step on the way to identifying the factors affecting the academic performance of the students. In this paper performance of FCBF is being evaluated on three different student’s datasets. The performance of FCBF is detected well on a student dataset with greater no of features.