Ensemble swarm based feature selection (ESFS) and ensemble three classifiers (ETCS) to predict student’s academic performance

Abstract

Recently Educational Data Mining (EDM) has attracted many researchers in recent years. Many techniques of data mining are formulated to generate the techniques of the knowledge that is hidden within the educational data. The knowledge which is extracted aid the educational institutions to enhance the teaching process and learning methods. These improvements enhance the student performance and the performance of overall outputs. In EDM, Feature Selection (FS) plays a significant role in the improvement of quality of the models used for the purpose of prediction of educational datasets. Single feature selection algorithms do not render enhanced results of prediction. In this proposed work, Ensemble Swarm based Feature Selection (ESFS) and Ensemble Three Classifiers (ETCs) is formulated to classify the performance of students based on the selected features. This work concentrates on ESFS techniques are formulated to select the important and intrinsic features before the process of classification, ETCs are proposed. The samples are selected from the knowledge repository, which is initially pre-processed by means of Min Max Normalization (MMN) and Z Score Normalization (ZCN) method. Then the selected attributes from the technique called Ensemble Swarm based Feature Selection (ESFS) are combined to the learner’s communication together with e-learning management system. ESFS algorithm fuses the Fuzzy Membership Genetic Algorithm (FMGA) and Improved Clonal Selection Algorithms (ICSAs). Also, Ensemble Three Classifiers (ETCs) is identified for the prediction of students’ performance by combining the qualifiers like Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) classifier and Decision Tree (DT). A widespread ensemble approach namely Bagging is utilized to combine all the results of three classifiers. The results that are obtained are found to have strong relationship among the learner’s behaviors and their academic achievement.