A Multi-Criteria Decision-Making Approach Enriched with Machine Learning: The Electre Tree Method and an Illustrative Example on Sustainable Development Education Indicators

Yıl 2025, Cilt: 9 Sayı: 1, 26 - 47, 20.04.2025

Hasan Arda Burhan

https://doi.org/10.59293/anadoluiid.1636927

Öz

The integration of Multi-Criteria Decision-Making (MCDM) methods with Machine Learning (ML) algorithms, which have become increasingly widespread in recent years, stands out as a significant development in improving the accuracy and dynamism of complex decision-making processes. This study mainly aims to introduce the ELECTRE Tree method, a classification approach based on MCDM-ML, to the Turkish literature which was first presented by Montenegro de Barros et al. (2021). The study includes an example illustrative application utilizing statistics published by the European Union's Statistical Office (EUROSTAT) to analyze indicators related to the fourth Sustainable Development Goal (SDG 4): Quality Education. According to the results, four distinct classes obtained, with Denmark, Ireland, the Netherlands, Finland, and Sweden emerging as the top-performing countries; while Türkiye was classified in the third group. The accurate classifications indicate that the integration of this method can provide effective.

Anahtar Kelimeler

ELECTRE Tree , MCDM , Machine Learning , Classification

Makine Öğrenmesi ile Zenginleştirilmiş Çok Kriterli Bir Karar Verme Yaklaşımı: Electre Ağacı Yöntemi ve Sürdürülebilir Kalkınma Eğitim Göstergeleri Üzerine Bir Örnek Uygulama

Öz

Çok Kriterli Karar Verme (ÇKKV) yöntemlerinin özellikle son yıllarda sıklıkla kullanılmaya başlayan Makine Öğrenmesi (Machine Learning (ML)) algoritmaları ile entegrasyonu, karmaşık karar verme süreçlerini daha doğru ve dinamik hale getiren önemli bir gelişme olarak öne çıkmaktadır. Bu çalışma, esas olarak Montenegro de Barros vd. (2021)’nin çalışmaları ile literatüre giren ancak henüz Türkçe literatürde kullanılmamış bir ÇKKV-ML temelli sınıflandırma yaklaşımı olan ELECTRE Ağacı (ELECTRE Tree) yönteminin tanıtılmasını amaçlamaktadır. Bu doğrultuda çalışmada Avrupa Birliği İstatistik Ofisinin (EUROSTAT) yayınladığı istatistiklerden yararlanılarak Sürdürülebilir Kalkınma Amaçları (SKA)’ndan 4.sü olan Nitelikli Eğitim amacına ait göstergelerin dahil olduğu bir de örnek illüstratif uygulama yapılmıştır. Örnek uygulama sonucunda dört farklı sınıf elde edilmiş ve SKA 4’e göre en başarılı ülkelerin Danimarka, İrlanda, Hollanda, Finlandiya ve İsveç olmuş; Türkiye ise üçüncü sınıfta yer almıştır. Elde edilen isabetli sınıflandırmalar uyarınca yöntemin etkin çözümler sunabileceği görülmüştür.

Anahtar Kelimeler

ELECTRE Ağacı , ÇKKV , Makine Öğrenmesi , Sınıflandırma

Kaynakça

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