COMPARISON OF PERFORMANCE OF DIFFERENT K WITH K-FOLD CROSS VALIDATION IN A GRAPH-BASED LEARNING MODEL FOR IncRNA-DISEASE PREDICTION

Authors : Zeynep BARUT, Volkan ALTUNTAŞ

Pages : 63-82

Doi:10.34186/klujes.1248062

View : 32 | Download : 36

Publication Date : 2023-06-30

Article Type : Research Paper

Abstract :In machine learning, the k value in the k-fold cross-validation method significantly affects the performance of the created model. In the studies that have been done, the k value is usually taken as five or ten because these two values are thought to produce average estimates. However, there is no official rule. It has been observed that few studies have been carried out to use different k values in the training of different models. In this study, a performance evaluation was performed on the IncRNA-disease model using various k values insert ignore into journalissuearticles values(2, 3, 4, 5, 6, 7, 8, 9, and 10); and datasets. The obtained results were compared and the most suitable k value for the model was determined. In future studies, it is aimed to carry out a more comprehensive study by increasing the number of data sets.
Keywords : Graf Otomatik Kodlayıcı, Varyasyonel Çıkarım, Temsil Öğrenimi