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An heuristic feature selection algorithm to evaluate academic performance of students

The value of schooling and academic performance of student is the topmost priority of all academic institutions. Educational Data Mining (EDM) is an evolving area of research which aids academic institutions to enhance their student's performances. Feature Selection algorithms eradicates inapt...

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Main Authors: Ajibade, S. S. M., Ahmad, N. B., Shamsuddin, S. M.
Format: Conference or Workshop Item
Language:English
Published: 2019
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/90771/1/SamuelSomaAjibade2019_AnHeuristicFeatureSelection.pdf
http://eprints.utm.my/id/eprint/90771/
http://dx.doi.org/10.1109/ICSGRC.2019.8837067
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spelling my.utm.907712021-04-30T14:30:33Z http://eprints.utm.my/id/eprint/90771/ An heuristic feature selection algorithm to evaluate academic performance of students Ajibade, S. S. M. Ahmad, N. B. Shamsuddin, S. M. QA75 Electronic computers. Computer science The value of schooling and academic performance of student is the topmost priority of all academic institutions. Educational Data Mining (EDM) is an evolving area of research which aids academic institutions to enhance their student's performances. Feature Selection algorithms eradicates inapt and unrelated data from the dataset, thereby increasing the classifiers performances that are utilized in EDM. This aim of this paper is to evaluate the performance of students utilizing a heuristic technique known as Differential Evolution for feature selection algorithms on the dataset of students and some other feature selection algorithms have also been used which have never been used before on the dataset. Also, classification techniques such as Naïve Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN) and Discriminant Analysis (DISC) were used to evaluate. The Differential Evolution (DE) algorithm is proposed as a better feature selection algorithm for evaluating the academic performance of students and this gave a better accuracy than other feature selection algorithm that were used. The outcome of the different feature selection algorithms and classification techniques will help researchers to find the finest combinations of the classifiers and feature selection algorithms. This paper is a step towards playing an important role in enhancing the standard of education in academic institutions and also to carefully guide researchers in strategically interfering in academic issues. 2019 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/90771/1/SamuelSomaAjibade2019_AnHeuristicFeatureSelection.pdf Ajibade, S. S. M. and Ahmad, N. B. and Shamsuddin, S. M. (2019) An heuristic feature selection algorithm to evaluate academic performance of students. In: 10th IEEE Control and System Graduate Research Colloquium, ICSGRC 2019, 2-3 Aug 2019, Shah Alam, Malaysia. http://dx.doi.org/10.1109/ICSGRC.2019.8837067
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Ajibade, S. S. M.
Ahmad, N. B.
Shamsuddin, S. M.
An heuristic feature selection algorithm to evaluate academic performance of students
description The value of schooling and academic performance of student is the topmost priority of all academic institutions. Educational Data Mining (EDM) is an evolving area of research which aids academic institutions to enhance their student's performances. Feature Selection algorithms eradicates inapt and unrelated data from the dataset, thereby increasing the classifiers performances that are utilized in EDM. This aim of this paper is to evaluate the performance of students utilizing a heuristic technique known as Differential Evolution for feature selection algorithms on the dataset of students and some other feature selection algorithms have also been used which have never been used before on the dataset. Also, classification techniques such as Naïve Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN) and Discriminant Analysis (DISC) were used to evaluate. The Differential Evolution (DE) algorithm is proposed as a better feature selection algorithm for evaluating the academic performance of students and this gave a better accuracy than other feature selection algorithm that were used. The outcome of the different feature selection algorithms and classification techniques will help researchers to find the finest combinations of the classifiers and feature selection algorithms. This paper is a step towards playing an important role in enhancing the standard of education in academic institutions and also to carefully guide researchers in strategically interfering in academic issues.
format Conference or Workshop Item
author Ajibade, S. S. M.
Ahmad, N. B.
Shamsuddin, S. M.
author_facet Ajibade, S. S. M.
Ahmad, N. B.
Shamsuddin, S. M.
author_sort Ajibade, S. S. M.
title An heuristic feature selection algorithm to evaluate academic performance of students
title_short An heuristic feature selection algorithm to evaluate academic performance of students
title_full An heuristic feature selection algorithm to evaluate academic performance of students
title_fullStr An heuristic feature selection algorithm to evaluate academic performance of students
title_full_unstemmed An heuristic feature selection algorithm to evaluate academic performance of students
title_sort heuristic feature selection algorithm to evaluate academic performance of students
publishDate 2019
url http://eprints.utm.my/id/eprint/90771/1/SamuelSomaAjibade2019_AnHeuristicFeatureSelection.pdf
http://eprints.utm.my/id/eprint/90771/
http://dx.doi.org/10.1109/ICSGRC.2019.8837067
_version_ 1698696983142727680
score 13.211869

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