IncRNA-HASTALIK TAHMİNİ İÇİN GRAPH TABANLI BİR ÖĞRENME MODELİNDE K-FOLD CROSS VALIDATION İLE FARKLI K DEĞERLERİNİN PERFORMANSININ KARŞILAŞTIRILMASI

Year 2023, Volume: 9 Issue: 1, 63 - 82, 30.06.2023

Zeynep Barut , Volkan Altuntaş

https://doi.org/10.34186/klujes.1248062

Cited By: 2

Abstract

Makine öğrenmesinde, k-katlı çapraz doğrulama yöntemindeki k değeri, oluşturulan modelin performansını önemli ölçüde etkilemektedir. Yapılmış olan çalışmalarda genellikle k değeri beş veya on alınmaktadır çünkü bu iki değerin ortalama tahminler ürettiği düşünülmektedir. Ancak resmi bir kural yoktur. Farklı modellerin eğitiminde farklı k değerlerinin kullanılması için az sayıda çalışma yapıldığı görülmüştür. Bu çalışmada, çeşitli k değerleri (2, 3, 4, 5, 6, 7, 8, 9 ve 10) ve veri setleri kullanılarak IncRNA-hastalık modeli üzerinde bir performans değerlendirilmesi yapılmıştır. Elde edilen sonuçlar karşılaştırılmış ve model için en uygun k değeri belirtilmiştir. Gelecekte yapılacak olan çalışmalarda veri seti sayısının arttırılması ile daha geniş kapsamlı bir çalışma yapılması hedeflenmektedir.

Keywords

Graf Otomatik Kodlayıcı, Varyasyonel Çıkarım, Temsil Öğrenimi

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

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 (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

Graph Autoencoder, Variational Inference, Representation Learning

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