Show pageBacklinksCite current pageExport to PDFBack to top This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong. ====== SMOTE ====== The Synthetic Minority Over-sampling Technique(SMOTE) to balance the data prior to feeding them into the network.10 [[Keras]]’ Adam optimizer was chosen with default parameters, i.e., learning rate 0.001, and dropout with a value of 0.1 was used to prevent overfitting ((Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 2014;15:1929-1958.)). Cross-validation on a 0.33 split was used on the class-balanced datasets during fitting the model. smote.txt Last modified: 2024/06/07 02:57by 127.0.0.1