Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
Random Ferns in the Classification of High Dimension Low Sample Size Settings: A Simulation Study
Yüksek Boyut Düşük Örneklem Genişliğine Sahip Verilerin Sınıflandırılmasında Random Ferns: Bir Benzetim Çalışması
Ülger AYDOĞAN CULHAa, Erdem KARABULUTb
aGeneral Directorate of Turkish Highways, Ankara, Türkiye
bDepartment of Biostatistics, Hacettepe University Faculty of Medicine, Ankara, Türkiye
Turkiye Klinikleri J Biostat. 2022;14(2):81-9
doi: 10.5336/biostatic.2022-89317
Article Language: EN
Full Text
ABSTRACT
 Objective: Nowadays, 'high dimension low sample size (HDLSS) settings' is very popular in many areas such as genetics, bioinformatics, medical imaging. In this study, it was aimed to investigate the classification performance of Random Ferns, which is a relatively new classifier, on HDLSS settings with different characteristics. Material and Methods: By simulation studies, artificial data sets that have different characteristics in terms of dimension, sample size, correlation structure, noise ratio and prevalence, were generated. Each scenario was iterated for 1,000 times and, classification performances of Random Ferns has been compared with support vector machines (SVM), which stand out with its high classification performance. Results: The performance variation of Random Ferns differs from SVM. It performed better at small sample size (n=20). When the F values are examined, it is seen that the classification performance of Random Ferns in the case of imbalanced distribution does not change much according to the balanced distribution situation and is higher than SVM. It has also been observed that high success as was expected in the classical data structure cannot be achieved. Conclusion: It is noteworthy that Random Ferns outperforms, especially in balanced distribution and small sample sizes. It is thought that this method, which does not have many applications in the field of health, will contribute to studies where the number of observations is quite low.

Keywords: Random Ferns; classification; machine learning; high dimensional data; simulation
ÖZET
 Amaç: Günümüzde 'yüksek boyut düşük örneklem genişlikli (YBDÖG) düzenler'; genetik, biyoinformatik, tıbbi görüntüleme gibi birçok alanda oldukça popülerdir. Bu çalışmada, nispeten yeni bir sınıflandırıcı olan Random Ferns algoritmasının farklı özelliklere sahip YBDÖG yapılar üzerindeki sınıflandırma performanslarının araştırılması amaçlanmıştır. Gereç ve Yöntemler: Benzetim teknikleri kullanılarak boyut, örneklem genişliği, korelasyon yapısı, gürültü oranı ve prevalans açısından farklı özelliklere sahip yapay veri setleri üretilmiştir. Her bir senaryo 1.000 kez tekrarlanmış ve Random Ferns algoritmasının sınıflandırma performansı, yüksek sınıflama başarısı ile bilinen destek vektör makineleri (DVM) ile karşılaştırılmıştır. Bulgular: Random Fernsvin performans değişimi DVM'den farklıdır. En belirgin özelliği, küçük örneklem düzeylerinde (n=20) daha iyi performans göstermesidir. F değerleri incelendiğinde, dengesiz dağılım durumunda Random Ferns'lerin sınıflandırma performansının dengeli dağılım durumuna göre çok fazla değişmediği ve DVM'den daha yüksek olduğu görülmektedir. Klasik veri yapılarında olduğu gibi yüksek başarının ise sağlanmadığı da görülmüştür. Sonuç: Random Ferns'ün özellikle dengeli dağılıma ve küçük örneklem genişliğine sahip yapılarda daha iyi performans göstermesi dikkat çekmektedir. Sağlık alanında fazla uygulaması olmayan bu yöntemin, gözlem sayılarının oldukça düşük olduğu çalışmalara katkı sağlayabileceği düşünülmektedir.

Anahtar Kelimeler: Random Ferns; sınıflandırma; makine öğrenmesi; yüksek boyutlu veriler; simülasyon
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