Cargando…
Proposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through data analytics
When it comes to choosing the best option among multiple alternatives with criteria of different importance, it makes sense to use multi criteria decision making (MCDM) methods with more than 200 variations. However, because the algorithms of MCDM methods are different, they do not always produce th...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10159627/ https://www.ncbi.nlm.nih.gov/pubmed/37153010 http://dx.doi.org/10.7717/peerj-cs.1350 |
_version_ | 1785037141008449536 |
---|---|
author | Baydaş, Mahmut Eren, Tevfik Stević, Željko Starčević, Vitomir Parlakkaya, Raif |
author_facet | Baydaş, Mahmut Eren, Tevfik Stević, Željko Starčević, Vitomir Parlakkaya, Raif |
author_sort | Baydaş, Mahmut |
collection | PubMed |
description | When it comes to choosing the best option among multiple alternatives with criteria of different importance, it makes sense to use multi criteria decision making (MCDM) methods with more than 200 variations. However, because the algorithms of MCDM methods are different, they do not always produce the same best option or the same hierarchical ranking. At this point, it is important how and according to which MCDM methods will be compared, and the lack of an objective evaluation framework still continues. The mathematical robustness of the computational procedures, which are the inputs of MCDM methods, is of course important. But their output dimensions, such as their capacity to generate well-established real-life relationships and rank reversal (RR) performance, must also be taken into account. In this study, we propose for the first time two criteria that confirm each other. For this purpose, the financial performance (FP) of 140 listed manufacturing companies was calculated using nine different MCDM methods integrated with step-wise weight assessment ratio analysis (SWARA). İn the next stage, the statistical relationship between the MCDM-based FP final results and the simultaneous stock returns of the same companies in the stock market was compared. Finally, for the first time, the RR performance of MCDM methods was revealed with a statistical procedure proposed in this study. According to the findings obtained entirely through data analytics, Faire Un Choix Adéquat (FUCA) and (which is a fairly new method) the compromise ranking of alternatives from distance to ideal solution (CRADIS) were determined as the most appropriate methods by the joint agreement of both criteria. |
format | Online Article Text |
id | pubmed-10159627 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101596272023-05-05 Proposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through data analytics Baydaş, Mahmut Eren, Tevfik Stević, Željko Starčević, Vitomir Parlakkaya, Raif PeerJ Comput Sci Data Science When it comes to choosing the best option among multiple alternatives with criteria of different importance, it makes sense to use multi criteria decision making (MCDM) methods with more than 200 variations. However, because the algorithms of MCDM methods are different, they do not always produce the same best option or the same hierarchical ranking. At this point, it is important how and according to which MCDM methods will be compared, and the lack of an objective evaluation framework still continues. The mathematical robustness of the computational procedures, which are the inputs of MCDM methods, is of course important. But their output dimensions, such as their capacity to generate well-established real-life relationships and rank reversal (RR) performance, must also be taken into account. In this study, we propose for the first time two criteria that confirm each other. For this purpose, the financial performance (FP) of 140 listed manufacturing companies was calculated using nine different MCDM methods integrated with step-wise weight assessment ratio analysis (SWARA). İn the next stage, the statistical relationship between the MCDM-based FP final results and the simultaneous stock returns of the same companies in the stock market was compared. Finally, for the first time, the RR performance of MCDM methods was revealed with a statistical procedure proposed in this study. According to the findings obtained entirely through data analytics, Faire Un Choix Adéquat (FUCA) and (which is a fairly new method) the compromise ranking of alternatives from distance to ideal solution (CRADIS) were determined as the most appropriate methods by the joint agreement of both criteria. PeerJ Inc. 2023-04-25 /pmc/articles/PMC10159627/ /pubmed/37153010 http://dx.doi.org/10.7717/peerj-cs.1350 Text en © 2023 Baydaş et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Data Science Baydaş, Mahmut Eren, Tevfik Stević, Željko Starčević, Vitomir Parlakkaya, Raif Proposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through data analytics |
title | Proposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through data analytics |
title_full | Proposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through data analytics |
title_fullStr | Proposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through data analytics |
title_full_unstemmed | Proposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through data analytics |
title_short | Proposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through data analytics |
title_sort | proposal for an objective binary benchmarking framework that validates each other for comparing mcdm methods through data analytics |
topic | Data Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10159627/ https://www.ncbi.nlm.nih.gov/pubmed/37153010 http://dx.doi.org/10.7717/peerj-cs.1350 |
work_keys_str_mv | AT baydasmahmut proposalforanobjectivebinarybenchmarkingframeworkthatvalidateseachotherforcomparingmcdmmethodsthroughdataanalytics AT erentevfik proposalforanobjectivebinarybenchmarkingframeworkthatvalidateseachotherforcomparingmcdmmethodsthroughdataanalytics AT steviczeljko proposalforanobjectivebinarybenchmarkingframeworkthatvalidateseachotherforcomparingmcdmmethodsthroughdataanalytics AT starcevicvitomir proposalforanobjectivebinarybenchmarkingframeworkthatvalidateseachotherforcomparingmcdmmethodsthroughdataanalytics AT parlakkayaraif proposalforanobjectivebinarybenchmarkingframeworkthatvalidateseachotherforcomparingmcdmmethodsthroughdataanalytics |