Evidence-Based Process in Research Involving Machine Learning Algorithms

Lukmanjaya, WK; Islam, MR; Xu, G

Evidence-Based Process in Research Involving Machine Learning Algorithms

Lukmanjaya, WK Islam, MR Xu, G

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022, 2022, 00, pp. 1-7
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Lukmanjaya, WK
dc.contributor.author	Islam, MR
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663
dc.date	2022-10-29
dc.date.accessioned	2023-04-18T02:05:26Z
dc.date.available	2023-04-18T02:05:26Z
dc.date.issued	2022-01-01
dc.identifier.citation	Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022, 2022, 00, pp. 1-7
dc.identifier.isbn	9798350398144
dc.identifier.uri	http://hdl.handle.net/10453/169894
dc.description.abstract	The current state of research involving the application of machine learning (ML) algorithms on various topics that directly impact human beings does not sufficiently focus on identifying and filling in research gaps. Evidence is lacking that the maximum accuracy showcased in multiple research papers is the best accuracy possible. However, it is vital to fill in research gaps to be cost-effective and actionable in the big picture. This study proposes a guideline that can be useful for future work involving ML algorithms on high-risk topics to fill in research gaps as much as possible for a particular problem. In this study, 10 different models were conducted with 12.7 million different parameter combinations to create this guideline. The results from the experiments demonstrated experimentation with different algorithms and parameters is crucial in research to deduct which algorithm performs well. Exposure to accurate and inaccurate models can assist researchers and relevant professionals in highlighting evidence-based methods that might contribute to improved findings in various areas. It suggests what works and does not and which task is the most appropriate to fill research gaps. It is also important to consider the guideline produced in this study as inspiration. More research is necessary to improve the guideline.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022
dc.relation.ispartof	2022 9th International Conference on Behavioural and Social Computing (BESC)
dc.relation.isbasedon	10.1109/BESC57393.2022.9995599
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Evidence-Based Process in Research Involving Machine Learning Algorithms
dc.type	Conference Proceeding
utslib.citation.volume	00
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-18T02:05:25Z
pubs.finish-date	2022-10-31
pubs.publication-status	Published
pubs.start-date	2022-10-29
pubs.volume	00

Abstract:

The current state of research involving the application of machine learning (ML) algorithms on various topics that directly impact human beings does not sufficiently focus on identifying and filling in research gaps. Evidence is lacking that the maximum accuracy showcased in multiple research papers is the best accuracy possible. However, it is vital to fill in research gaps to be cost-effective and actionable in the big picture. This study proposes a guideline that can be useful for future work involving ML algorithms on high-risk topics to fill in research gaps as much as possible for a particular problem. In this study, 10 different models were conducted with 12.7 million different parameter combinations to create this guideline. The results from the experiments demonstrated experimentation with different algorithms and parameters is crucial in research to deduct which algorithm performs well. Exposure to accurate and inaccurate models can assist researchers and relevant professionals in highlighting evidence-based methods that might contribute to improved findings in various areas. It suggests what works and does not and which task is the most appropriate to fill research gaps. It is also important to consider the guideline produced in this study as inspiration. More research is necessary to improve the guideline.

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http://hdl.handle.net/10453/169894