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https://doi.org/10.31341/jios.48.1.7

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

Muhammad Arham Tariq orcid id orcid.org/0000-0002-6827-4177 ; Department of Computer Science, University of Central Punjab, Lahore, Pakistan


Puni tekst: engleski pdf 480 Kb

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str. 133-147

preuzimanja: 0

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Sažetak

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.

Ključne riječi

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

Hrčak ID:

318118

URI

https://hrcak.srce.hr/318118

Datum izdavanja:

16.6.2024.

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