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Dergiler / Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 32 Sayı: 6

Öznitelik seçimi için immün plazma algoritmasına dayalı yeni bir metasezgisel yaklaşım: IPAbFS

A new metaheuristic approach for feature selection based on immune plasma algorithm: IPAbFS

Özer Oğuz(KAHRAMANMARAS SUTCU IMAM UNIVERSITY)
Hasan Badem(KAHRAMANMARAS SUTCU IMAM UNIVERSITY)
Sayfa
1274–1284
Yayın Tarihi
2026-10-08
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Deney sonuçları, IPAbFS'nin sınıflandırma doğruluğu ve özellik azaltma açısından on adet rakip metaheuristik FS algoritmasından önemli ölçüde daha iyi performans sergilediğini göstermektedir.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Özellik seçimi (FS), makine öğreniminde, alakasız ve gereksiz özellikleri kaldırarak model performansını artıran kritik bir ön işleme adımıdır.
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Temel Bulgular & Veri:Önerilen IPAbFS'nin performansı, çeşitli alanlarda 39 farklı veri seti üzerinde kapsamlı bir şekilde değerlendirilmiştir.
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Sonuç & Katkı:Deney sonuçları, IPAbFS'nin sınıflandırma doğruluğu ve özellik azaltma açısından on adet rakip metaheuristik FS algoritmasından önemli ölçüde daha iyi performans sergilediğini göstermektedir.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Öznitelik seçimi · İmmün plazma algoritması · Makine öğrenmesi

Özet

Özellik seçimi (FS), makine öğreniminde, alakasız ve gereksiz özellikleri kaldırarak model performansını artıran kritik bir ön işleme adımıdır. Bu işleme çeşitli metaheuristik algoritmalar uygulanmış olsa da yüksek boyutlu veriler için daha başarılı yöntemler bulmak üzere araştırmalar devam etmektedir. Bu çalışma, İmmün Plazma Algoritması (IPA) temelinde yeni bir özellik seçimi yaklaşımı olan IPAbFS'yi önermektedir. Önerilen IPAbFS'nin performansı, çeşitli alanlarda 39 farklı veri seti üzerinde kapsamlı bir şekilde değerlendirilmiştir. Sonuçların güvenilirliğini sağlamak için, K-En Yakın Komşular (KNN), Karar Ağacı (DT) ve Destek Vektör Makineleri (SVM) sınıflandırıcıları kullanılarak 10 kat çapraz doğrulama uygulanmıştır. Deney sonuçları, IPAbFS'nin sınıflandırma doğruluğu ve özellik azaltma açısından on adet rakip metaheuristik FS algoritmasından önemli ölçüde daha iyi performans sergilediğini göstermektedir.

Abstract

Feature selection (FS) is a critical preprocessing step in machine learning that enhances model performance by removing irrelevant and redundant features. Although various metaheuristic algorithms have been applied to this process, research continues to find more successful methods for high-dimensional data. This study proposes IPAbFS, a new feature selection approach based on the Immune Plasma Algorithm (IPA). The performance of the proposed IPAbFS has been comprehensively evaluated on 39 different datasets across various domains. To ensure the reliability of the results, 10-fold cross-validation was applied using K-Nearest Neighbors (KNN), Decision Tree (DT), and Support Vector Machines (SVM) classifiers. The experimental results show that IPAbFS significantly outperforms ten competing metaheuristic FS algorithms in terms of classification accuracy and feature reduction.

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Bu Makaleye Atıf Yap

Oğuz, Ö., Badem, H. (2026). Öznitelik seçimi için immün plazma algoritmasına dayalı yeni bir metasezgisel yaklaşım: IPAbFS. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 32 Sayı: 6, 1274–1284. https://doi.org/10.65206/pajes.34614

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