This preprint is under consideration at Soft Computing. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
Research Article
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Students’ performance prediction systems play a vital role in enhancing the educational performance inside universities, schools, and training centers. Big data can come from different resources such as exam centers, virtual courses, registration departments, e-learning systems, and so on. Extracting meaningful knowledge from educational data is a complex task, so, reducing the data dimensionality is needed. In this paper, we proposed an enhanced binary genetic algorithm (EBGA) as a wrapper feature selection algorithm. Novel hybrid selection mechanism based on a k-means algorithm and Electromagnetic-like mechanism (EM) method is proposed. K-means will cluster the population into a set of clusters, while EM will determine a value called a total force (TF) for each solution. Each cluster has an accumulated total force (ATF) (i.e., adding all TFs together). Selection process will select two solutions with the highest TF from the cluster, which has the highest ATF. We employed a hybrid machine learning approach between the proposed EBGA and five different classifiers (i.e., k-Nearest Neighbors (k-NN), Decision Trees (DT), Naive Bayes (NB), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA)). Two real case studies obtained from UCI Machine Learning Repository are used in this paper. Obtained results showed the ability of the proposed approach to enhance the performance of the binary genetic algorithm. Moreover, the performances of all classifiers are improved between 1% to 11%.
Keywords
Genetic algorithm, Feature selection, Students performance, Electromagnetic-like mechanism
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
The full text of this article is available to read as a PDF.
Loading...
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 10 May, 2021
No community comments so far
Reviews received
Received 06 May, 2021
Reviewers invited
Invitations sent on 05 May, 2021
First submitted
On 25 Jan, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Theoretical Computer Science
Learn more about our company.
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
This preprint is under consideration at Soft Computing. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 10 May, 2021
No community comments so far
Reviews received
Received 06 May, 2021
Reviewers invited
Invitations sent on 05 May, 2021
First submitted
On 25 Jan, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Theoretical Computer Science
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Students’ performance prediction systems play a vital role in enhancing the educational performance inside universities, schools, and training centers. Big data can come from different resources such as exam centers, virtual courses, registration departments, e-learning systems, and so on. Extracting meaningful knowledge from educational data is a complex task, so, reducing the data dimensionality is needed. In this paper, we proposed an enhanced binary genetic algorithm (EBGA) as a wrapper feature selection algorithm. Novel hybrid selection mechanism based on a k-means algorithm and Electromagnetic-like mechanism (EM) method is proposed. K-means will cluster the population into a set of clusters, while EM will determine a value called a total force (TF) for each solution. Each cluster has an accumulated total force (ATF) (i.e., adding all TFs together). Selection process will select two solutions with the highest TF from the cluster, which has the highest ATF. We employed a hybrid machine learning approach between the proposed EBGA and five different classifiers (i.e., k-Nearest Neighbors (k-NN), Decision Trees (DT), Naive Bayes (NB), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA)). Two real case studies obtained from UCI Machine Learning Repository are used in this paper. Obtained results showed the ability of the proposed approach to enhance the performance of the binary genetic algorithm. Moreover, the performances of all classifiers are improved between 1% to 11%.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
The full text of this article is available to read as a PDF.
Loading...