Yüksek Boyut Düşük Örneklem Genişliğine Sahip Verilerin Sınıflandırılmasında Random Ferns: Bir Benzetim Çalışması

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.