HYBRID METHOD USING DIFFERENTIAL EVOLUTION ALGORITHM AND ARTIFICIAL BEE COLONY OPTIMIZATION TECHNIQUE FOR FEATURE SELECTION PROBLEM

Abstract

Aim of the feature selection process is to find the best features which can represent the dataset by narrowing the feature space optimally. This study proposes a new hybrid method which combines Artificial Bee Colony and Differential Evolution algorithms for feature selection problem of classification tasks. The proposed algorithm was tested using decision tree classifier (J48) on UCI datasets. The experimental results show that the new hybrid method reduces the number of features by not decreasing or least decreasing the classification performance and therefore the time which it takes for classification of new instances decreases as well

Keywords

• Feature selection, Hybrid optimization, Artificial bee colony, Differential evolution

NITELIK SEÇME PROBLEMI IÇIN DIFERANSIYEL GELIŞIM ALGORITMASI VE YAPAY ARI KOLONISI OPTIMIZASYON TEKNIĞINI KULLANAN MELEZ YÖNTEM

Abstract

Nitelik seçme işlemi ile özellik uzayı optimum şekilde daraltılarak veri kümesini en iyi şekilde temsil edebilecek niteliklerin bulunması amaçlanır. Bu çalışma sınıflandırma işlemleri üzerinde nitelik seçme problemi için Yapay Arı Kolonisi optimizasyon tekniği ve Diferansiyel Gelişim algoritmasını birleştirerek yeni bir melez yöntem önermektedir. Önerilen algoritma UCI veri kümeleri üzerinde karar ağacı sınıflandırıcısı (J48) kullanılarak test edilmiştir. Deneysel sonuçlar yeni melez yöntemin sınıflandırma işleminin doğruluğunu düşürmeden ya da en az seviyede düşürerek nitelik sayısını azalttığını ve dolayısıyla yeni örneklerin sınıflandırılması için gereken sürenin de azaldığını göstermiştir.

Keywords

• Nitelik seçme, Hibrit optimizasyon, Yapay arı kolonisi, Diferansiyel gelişim

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