Optimizing Neural Networks for Academic Performance Classification Using Feature Selection and Resampling Approach

Simple item page
dc.contributor.authorSupriyadi, Didi
dc.contributor.authorPurwanto, Purwanto
dc.contributor.authorWarsito, Budi
dc.coverage.issue2cs
dc.coverage.volume29cs
dc.date.accessioned2024-01-11T09:48:06Z
dc.date.available2024-01-11T09:48:06Z
dc.date.issued2023-12-31cs
dc.description.abstractThe features present in large datasets significantly affect the performance of machine learning models. Redundant and irrelevant features will be rejected and cause a decrease in machine learning model performance. This paper proposes HyFeS-ROS-ANN: Hybrid Feature Selection and Resampling combination method for binary classification using artificial neural network multilayer perceptron (MLP). The first stage of this approach is to use a combination of two feature selection methods to select essential features that are highly correlated with model performance. The second stage of this approach is to use a combination of resampling methods to handle unbalanced data classes. Both approaches are applied to the academic performance classification model using the MLP neural network. This research dataset is obtained using three-dimensional (3D) frameworks such as the Big Five Personality to determine the Personality that affects academic performance from the student dimension, the Family Influence Scale (FIS), which measures factors that affect academic performance from the family dimension, and Higher Education Institutions Service Quality (HEISQUAL) to measure service quality and its influence on academic performance from the Education institution dimension. Previous research shows that the CoR-ANN algorithm has a model accuracy rate of 94%. The research results based on the dataset show that our proposed method can improve accuracy by selecting more relevant and essential features in improving model performance. The results show that the features are reduced from 135 to 108, while the HyFS-ROS-ANN model for binary classification accuracy increases to 100%.en
dc.formattextcs
dc.format.extent261-272cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMendel. 2023 vol. 29, č. 2, s. 261-272. ISSN 1803-3814cs
dc.identifier.doi10.13164/mendel.2023.2.261en
dc.identifier.issn2571-3701
dc.identifier.issn1803-3814
dc.identifier.urihttps://hdl.handle.net/11012/244253
dc.language.isoencs
dc.publisherInstitute of Automation and Computer Science, Brno University of Technologycs
dc.relation.ispartofMendelcs
dc.relation.urihttps://mendel-journal.org/index.php/mendel/article/view/295cs
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0en
dc.subjectFeature selectionen
dc.subjectImbalanced dataseten
dc.subjectResampling approachen
dc.subjectNeural networken
dc.subjectAcademic performanceen
dc.subjectPersonalityen
dc.subjectFamilyen
dc.subjectService qualityen
dc.titleOptimizing Neural Networks for Academic Performance Classification Using Feature Selection and Resampling Approachen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
276-Article Text-848-2-10-20231219.pdf
Size:
2.33 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Vol. 29, No. 2