Makine öğrenimi ve ÇKKV tekniklerinin birleştirilmesi: genişletilmiş bir bibliyometrik analiz

Yıl 2024, Cilt: 4 Sayı: 2, 642 - 657, 31.07.2024

Mehmet Asaf Düzen , İsmail Buğra Bölükbaşı , Eyüp Çalık

https://doi.org/10.61112/jiens.1475948

Öz

Makine Öğrenmesi (ML) ve Çok Kriterli Karar Verme (MCDM) son zamanlarda birçok farklı alanda yaygın olarak kullanılan popüler yöntemlerdir. Bu iki yöntemin birlikte kullanımının artması nedeniyle bu alanda bibliyometrik bir analize ihtiyaç duyulmaktadır. Bu çalışmada, Ocak 2000 ile Nisan 2024 tarihleri arasında Web of Science (WoS) ve Scopus veri tabanlarından alınan 1189 makale üzerinde yazar tarafından geliştirilen genişletilmiş bir bibliyometrik analiz gerçekleştirilmiştir. İlk bibliyometrik analizde, genel bir bölüm olarak, verileri anlamlı hale getirmek için VOSviewer programı kullanılmıştır. Özellikle anahtar kelime analizi ile ilgili yıllara ve ilişkilere göre analiz gerçekleştirilmiştir. Ayrıca, en sık kullanılan anahtar kelimeler belirlenmiş ve eğilimin yönü tespit edilmiştir. İlk bibliyometrik analiz sırasında, WoS veri tabanından 297 yayın ve Scopus'tan 11 yayın olmak üzere 308 makale analiz edilmiştir. Çalışma, bibliyometrik analizin genişletilmiş bir parçası olarak yeni modeller ve kategoriler oluşturarak mevcut literatürden ayrılıyor. Bu model ve kategorileri kullanarak, araştırmacıların makine öğrenimi ve ÇKKV'yi birlikte nasıl kullandıkları ve bu yöntemlerin ne yönde geliştiği sorularına yanıt aradık. Bu bağlamda, model ve kategorilerin farklı araştırma alanlarındaki dağılımı ve yıllar içindeki değişimleri analiz edildi. Bu çalışma, araştırmacılara makine öğrenimi ve ÇKKV tekniklerini entegre ederken çeşitli kombinasyon olasılıkları hakkında kapsamlı bir bakış açısı sunmaktadır.

Anahtar Kelimeler

Makine öğrenmesi, Çok kriterli karar verme, Bibliyometrik analiz, VOSviewer

How to combine ML and MCDM techniques: an extended bibliometric analysis

Öz

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.

Anahtar Kelimeler

Machine learning, Multi criteria decision making, Bibliometric analysis, VOSviewer

Kaynakça

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