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Machine learning algorithm, scaling technique and the accuracy: an application to educational data.

Wickramasinghe, Indika; Kalutarage, Harsha

Authors

Indika Wickramasinghe



Abstract

Machine learning (ML) applications in educational data mining have become an increasingly popular research area. Literature indicates a lack of research investigating the impact of data scaling techniques, ML algorithms, and the nature of data on the classification's accuracy. This study aims to fulfill the above. In that direction, we use three linear and three non-linear ML classifiers and six scaling techniques to evaluate the impact of the data scaling technique and the ML algorithm on four selected educational datasets. According to the experimental outcomes for data set #1, classification accuracy was significantly influenced (p-value < 0.01) by the nature of the data. All the performance indicators except detection rate and prevalence were highly influenced by the type of ML technique used for the classification. Furthermore, there was a significant (p-value < 0.05) interaction impact of two-way interactions of the nature of the data and the type of ML technique for F1 value and balanced accuracy. Further analysis indicates that the classification accuracy varies with the level of the class variable.

Citation

WICKRAMASINGHE, I. and KALUTARAGE, H. 2024. Machine learning algorithm, scaling technique and the accuracy: an application to educational data. In: Proceedings of the 12th International conference on information and education technology 2024 (ICIET 2024) 18-20 March 2024, Yamaguchi, Japan. Piscataway: IEEE [online], pages 6-12. Available from: https://doi.org/10.1109/iciet60671.2024.10542714

Conference Name 12th International conference on information and education technology 2024 (ICIET 2024)
Conference Location Yamaguchi, Japan
Start Date Mar 18, 2024
End Date Mar 20, 2024
Acceptance Date Feb 5, 2024
Online Publication Date Mar 18, 2024
Publication Date Dec 31, 2024
Deposit Date Jun 6, 2024
Publicly Available Date Jun 6, 2024
Publisher Institute of Electrical and Electronics Engineers (IEEE)
DOI https://doi.org/10.1109/iciet60671.2024.10542714
Keywords Machine learning; Educational data mining; Educational data analysis; Data normalizing; Scaling techniques
Public URL https://rgu-repository.worktribe.com/output/2368201

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Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/

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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.





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