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

Öz

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.

Anahtar Kelimeler

• Feature selection
• Immune plasma algorithm
• Machine learning
• Optimization
• Classification

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

Öz

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.

Anahtar Kelimeler

• Öznitelik seçimi
• İmmün plazma algoritması
• Makine öğrenmesi
• Optimizasyon
• Sınıflandırma

Kaynakça

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