MULTIMOORA ile En İyi Makine Öğrenimi Algoritmasının Seçimi ve Covid-19 Pandemisi için Dünya Çapında Ülke Kümelerinin Belirlenmesi

Öz

Bu çalışmada, çok amaçlı karar vermeye dayalı kümeleme analizine entegre bir yaklaşım sunmak amacıyla, 27 iç geçerlilik kriterinin tamamı MULTIMOORA yöntemi ile eş zamanlı olarak değerlendirilerek 11 farklı kümeleme algoritması arasından en iyi kümeleme algoritmasının belirlenmesi amaçlanmıştır. Çalışmada öncelikle iki veri kümesi için en uygun küme sayısı ve bu küme sayısına bağlı olarak en iyi kümeleme algoritması belirlenmiştir. Daha sonra, belirlenen ülke kümelerinin insani gelişmişlik sınıflarıyla ilişkisinin belirlenmesine odaklanılmıştır. Yapılan analizler sonucunda COVID-19 salgınından etkilenen ülkeler, Öklid uzaklığı aracılığıyla hesaplanan yakınlıklarına göre CLARA ve SOM algoritmaları ile kümelenmiştir. Her iki veri kümesi için de en uygun küme sayısı olarak üç küme belirlenmiştir. Vaka-ölüm oranına kıyasla insidans oranının kümeler arasındaki gerçek farkta daha baskın faktör olduğu bulunmuştur. Bir diğer dikkat çekici bulgu ise, ekonomik gücü ve insani gelişmişlik düzeyi yüksek ülkelerin, aşılama öncesinde pandemiden daha az etkilenmesi beklenirken, insani gelişmişlik düzeyi yüksek olan ülkelerin pandemiden etkilenme düzeyinin her değişken bakımından da yüksek olmasıdır.

Anahtar Kelimeler

• Makine Öğrenimi
• Kümeleme
• İç geçerlilik kriterleri
• MULTIMOORA
• COVID-19

Determination of Worldwide Country Clusters by Selecting the Best Machine Learning Algorithm via MULTIMOORA for Covid-19 Pandemic

Öz

In this study, to present an integrated approach to clustering analysis based on multi-objective decision making, it is aimed to determine the best clustering algorithm among 11 different clustering algorithms by evaluating all 27 internal validity criteria simultaneously with MULTIMOORA method. In the study, initially, the best clustering algorithm was determined according to the optimal number of clusters for two COVID-19 datasets. Then, it focuses on determining the relationship of the country clusters with the classes determined according to the human development index. In the result of the analyses, countries affected by the COVID-19 pandemic have clustered via the CLARA and SOM algorithms according to their proximity calculated from the Euclidean distance. Three optimal number of clusters were determined for both datasets. The incidence rate variable is the more dominant factor than case fatality rate in the real difference between clusters. Another remarkable finding is that while countries with economic power and a high level of human development are expected to be less affected by the pandemic before the vaccination, the level of being affected by the pandemic increases in terms of both variables as the level of human development increases.

Anahtar Kelimeler

• Machine learning
• Clustering
• Internal validation criteria
• MULTIMOORA
• COVID-19

Kaynakça

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