KONTROLLÜ DENGESİZLİK SENARYOLARINDA TOPLULUK ÖĞRENME MODELLERİN SİSTEMATİK KARŞILAŞTIRMASI

Year 2025, Volume: 9 Issue: 1, 41 - 50, 30.06.2025

Muhammed Abdulhamid Karabıyık

https://doi.org/10.62301/usmtd.1701938

Abstract

Bu çalışma, sınıf dengesizliğinin topluluk öğrenme algoritmaları üzerindeki etkisini kontrollü bir deneysel tasarım ile incelemeyi amaçlamaktadır. Çalışma kapsamında, Iris ve Wine veri setleri üzerinde dört farklı sınıf dağılımı senaryosu (orijinal, hafif, orta ve şiddetli dengesizlik) uygulanmış ve her senaryoda Random Forest, Gradient Boosting ve Bagging algoritmaları test edilmiştir. Değerlendirmelerde yalnızca doğruluk değil, aynı zamanda Macro-F1, Balanced Accuracy, G-Mean ve Cohen Kappa gibi çoklu performans metrikleri kullanılmıştır. Elde edilen bulgular, Gradient Boosting modelinin yüksek dengesizlik düzeylerinde ciddi performans kayıpları yaşadığını; buna karşılık Random Forest algoritmasının tüm senaryolarda kararlı ve güvenilir sonuçlar sunduğunu ortaya koymuştur. Bu yönüyle çalışma, sınıf dengesizliğine karşı dayanıklı model seçiminin ve çok boyutlu metriklerle yapılan değerlendirmelerin önemini vurgulamaktadır.

Keywords

Sınıf dengesizliği , topluluk öğrenme , Random Forest , performans metrikleri , dengesiz veri setleri

A SYSTEMATIC COMPARISON OF ENSEMBLE MODELS UNDER CONTROLLED CLASS IMBALANCE SCENARIOS

Abstract

This study aims to investigate the impact of class imbalance on ensemble learning algorithms through a controlled experimental design. Four different class distribution scenarios (original, mild, moderate, and severe imbalance) were applied to the Iris and Wine datasets, and three ensemble models Random Forest, Gradient Boosting, and Bagging were evaluated in each scenario. Model performance was assessed using not only accuracy but also multiple metrics such as Macro-F1, Balanced Accuracy, G-Mean, and Cohen’s Kappa. The findings reveal that Gradient Boosting suffered significant performance degradation under high imbalance conditions, while Random Forest consistently delivered stable and reliable results across all scenarios. These results highlight the importance of selecting imbalance-resilient models and conducting evaluations using multiple performance indicators in imbalanced classification tasks.

Keywords

Class imbalance , ensemble learning , Random Forest , performance metrics , imbalanced datasets

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