A model for improved performance prediction using ensemble-based hybrid classification approach on a multivariate student dataset

Abstract

Classification techniques have sensed substantial attention in Information Engineering and Technology for the performance prediction and optimisation since few decades. The discovered accuracy of the Classification Model helps the institutional practices and student’s performances. In this paper, a novel Ensemble-based Hybrid Classification Approach (EHCA) has been proposed to be managed to produce improved performance prediction. The mining process with new attributes based on student behaviours has also been incorporated since it creates a great impact on their academic performances. Moreover, the performance of the students is analysed with a set of classifiers in Educational Data Mining (EDM) namely, Naive Bayesian, Support-Vector-Machine(SVM) and J48. Additionally, Ensemble approach is employed for enhancing the classifier performances. Here, the basic Ensemble methods such as Bagging, Classification Boosting and Stacking are used for optimising the results with more precision. Further, the process of Ensemble-based Hybrid Classification is analysed and tested with the dataset collected from Kerala Technological University-SNG College of Engineering(KTU_SNG). The results obtained are compared with the results obtained for utilized single classifiers and the EHCA on the basis of performance efficiency and classification accuracy. The work evidences the efficiency of the proposed approach and proves its reliability in Profound Performance Prediction and Optimisation.