DEB-ADA: DENGESİZ VERİLERLE BAŞA ÇIKMAK İÇİN HİBRİT BİR YAKLAŞIM

Öz

Denetimli öğrenme alanındaki sınıflandırma algoritmaları, sınıflar arası örnek sayılarının birbirine yakın olduğu dengeli verilerde en verimli şekilde çalışacak şekilde tasarlanmıştır. Bununla birlikte, gerçek dünya uygulamalarına bakıldığında, bazı sınıfların örnek sayıları diğer sınıflara kıyasla çok az veya çok fazla olabilir. Bu dengesizlik, sınıflandırma için eğitilmiş modellerin güvenilmez sonuçlar üretmesine neden olabilir. Bu çalışmada, dengesiz verilerle başa çıkmak için DEB ADA adı verilen yeni bir hibrit yaklaşım önerilmiştir. Yöntem, ADASYN ve DEBOHID algoritmalarının ardışık uygulanmasına dayanmaktadır. Hibrit yaklaşımda kullanılan ADASYN yöntemi, yüksek zorluk derecesine sahip azınlık sınıfı örneklerinin etrafında daha fazla sentetik örnek üreterek karar sınırlarını iyileştirir. Ayrıca, önerilen yaklaşımda DEBOHID yönteminin kullanılması aşırı uyum riskini azaltır. Önerilen yaklaşımın başarısı, KEEL veri deposundan alınan 44 dengesiz veri kümesi üzerinde test edilmiş ve Karar Ağacı (DT) sınıflandırıcı kullanılarak AUC ve G ortalama metrikleri ile değerlendirilmiştir. Deneysel sonuçlar, DEB ADA'nın literatürde yaygın olarak kullanılan klasik ve hibrit yöntemlere kıyasla daha tutarlı ve üstün sonuçlar verdiğini göstermektedir. Elde edilen bulgular, önerilen yöntemin dengesiz veri sınıflandırma problemine sağlam ve güvenilir bir çözüm sunduğunu göstermektedir.

Anahtar Kelimeler

• Aşırı Örnekleme
• Az Örnekleme
• Dengesiz Öğrenme
• ADASYN
• DEBOHID

Destekleyen Kurum

Bu çalışmanın hazırlanması aşamasında herhangi bir kurumdan destek alınmamıştır.

Etik Beyan

Bu çalışmanın hazırlık sürecinde bilimsel ve etik ilkelere uyulduğunu ve yararlanılan tüm çalışmaların kaynakçada belirtildiğini beyan ederim.

DEB-ADA: A HYBRID APPROACH TO DEALING WITH IMBALANCED DATA

Öz

Classification algorithms in the field of supervised learning are designed to operate most efficiently on balanced data where the sample numbers across classes are close. However, when examining real-world applications, the sample numbers for some classes can be very few or very many compared to other classes. This imbalance can cause models trained for classification to produce unreliable results. In this study, a novel hybrid approach called DEB-ADA is proposed to deal with imbalanced data. The method is based on the sequential implementation of the ADASYN and DEBOHID algorithms. The ADASYN method used in the hybrid approach improves decision boundaries by generating more synthetic samples around the minority class examples with high difficulty. Furthermore, the use of the DEBOHID method in the proposed approach reduces the risk of overfitting. The success of the proposed approach was tested on 44 imbalanced datasets from the KEEL data repository and evaluated using the AUC and G-mean metrics using a Decision Tree (DT) classifier. Experimental results show that DEB-ADA delivers more consistent and superior results compared to classical and hybrid methods commonly used in literature. The findings demonstrate that the proposed method offers a robust and reliable solution to the imbalanced data classification problem.

Anahtar Kelimeler

• Oversampling
• Undersampling
• Imbalanced Learning
• ADASYN
• DEBOHID

Destekleyen Kurum

No support was received from any institution during the preparation of this study.

Etik Beyan

It is declared that during the preparation process of this study, scientific and ethical principles were followed, and all the studies benefited from are stated in the bibliography.

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