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A Study on Comparative Analysis of Feature Selection Algorithms for Students Grades Prediction.
- Source :
-
Journal of Information & Organizational Sciences . Jun2024, Vol. 48 Issue 1, p133-147. 15p. - Publication Year :
- 2024
-
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(SFWEF) 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 18463312
- Volume :
- 48
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Journal of Information & Organizational Sciences
- Publication Type :
- Academic Journal
- Accession number :
- 179344532
- Full Text :
- https://doi.org/10.31341/jios.48.1.7