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A Study on Comparative Analysis of Feature Selection Algorithms for Students Grades Prediction

Journal: Journal of Information and Organizational Sciences (JIOS) (Vol.48, No. 1)

Publication Date:

Authors : ;

Page : 133-147

Keywords : Classification Models; Educational Data Mining; Features Selection; Multi-Class Datasets; Student Performance;

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Abstract

Education data mining (EDM) applies data mining techniques to extract insights from educational data, enabling educators to evaluate their teaching methods and improve student outcomes. Feature selection algorithms play a crucial role in improving classifier accuracy by reducing redundant features. However, a detailed and diverse comparative analysis of feature selection algorithms on multiclass educational datasets is missing. This paper presents a study that compares ten different feature selection algorithms for predicting student grades. The goal is to identify the most effective feature selection technique for multi-class student grades prediction. Five classifiers, including Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), Gradient Boosting (GB), and k-Nearest Neighbors (KNN), are trained and tested on ten different feature selection algorithms. The results show that SelectFwe(SFWE-F) performed best, achieving an accuracy of 74.3% with Random Forests (RT) across all ten feature selection algorithms. This algorithm selects features based on their relationship with the target variable while controlling the family-wise error rate.

Last modified: 2024-07-10 15:44:11