How to combine ML and MCDM techniques: an extended bibliometric analysis
Yazarlar (3)
Arş. Gör. Mehmet Asaf DÜZEN Yalova Üniversitesi, Türkiye
Arş. Gör. İsmail Buğra BÖLÜKBAŞI Yalova Üniversitesi, Türkiye
Doç. Dr. Eyüp ÇALIK Yalova Üniversitesi, Türkiye
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Özgün Makale
(Ulusal alan endekslerinde (TR Dizin, ULAKBİM) yayınlanan tam makale)
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| Dergi Adı | Journal of Innovative Engineering and Natural Science | ||
| Dergi ISSN | 2791-7630 | ||
| Dergi Tarandığı Indeksler | TR DİZİN | ||
| Makale Dili | İngilizce | Basım Tarihi | 07-2024 |
| Kabul Tarihi | 22-07-2024 | Yayınlanma Tarihi | 22-07-2024 |
| Cilt / Sayı / Sayfa | 4 / 2 / 642–657 | DOI | 10.61112/jiens.1475948 |
| Makale Linki | http://dx.doi.org/10.61112/jiens.1475948 | ||
| UAK Araştırma Alanları |
Makine Öğrenmesi
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| Özet |
| Machine Learning (ML) and Multi Criteria Decision Making (MCDM) are popular methods that have recently been widely used in many different fields. Due to the increasing use of these two methods together, there is a need for a bibliometric analysis in this area. In this study, an extended author-developed bibliometric analysis was performed on 1189 publications retrieved from the Web of Science (WoS) and Scopus databases between January 2000 and April 2024. In the initial bibliometric analysis, as a generic part, the VOSviewer program was used to make the data meaningful. In particular, the analysis was carried out according to years and relationships related to the keyword analysis. In addition, the most frequently used keywords were identified, and the direction of the trend was determined. During the initial bibliometric analysis, 308 publications were analyzed, with 297 publications retrieved from the WoS database and 11 publications from Scopus. The study distinguishes itself from the existing literature by establishing new models and categories as an extended part of bibliometric analysis. Using these models and categories, we sought to answer questions about how researchers use ML and MCDM together and in what direction these methods are evolving. In this context, the distribution of models and categories in different research areas and their changes over the years were analyzed. This study provides researchers with a comprehensive perspective on the various combination possibilities when integrating ML and MCDM techniques. |
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| Google Scholar | 3 |