Optimized classification of student performance outcomes using LEE feature selection in the context of educational data mining

Abstract

Student speculative victory is a vital area that needs to be predicted to improve the quality of education and aid the institutional decision making. This research work has to planned to use learning based enhanced evaluation (LEE) feature selection method with real world educational datasets for optimized data mining approach to predict student performance. High dimensionality and irrelevant features are common problems with enhanced models, affecting classification accuracy and efficiency. LEE feature algorithm is used to extract important features, that enhance the performance of the model, reduce the calculation quantity of the model. The methodology consists of pre-processing of the dataset, feature selection using LEE algorithm, and testing four classifiers namely support vector machine (SVM), k-nearest neighbor (KNN), adaptive learning, and naïve Bayes. The incorporation of LEE improves the model’s ability by reducing noise and highlighting the influential features. Experimental results show that optimized techniques are better in terms of accuracy and robustness than others. The models are evaluated based on important performance metrics such as accuracy, precision, recall, F1-score, and training time. The enhanced approach will help to add to the literature of the field of educational data mining (EDM), providing a practical and effective way of predicting student performance in real academic settings.