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Table 1 Summarizes the hyper-parameters of deep neural networks (e.g., convolutional neural networks) and LACFNForest, where the default values used in our experiments are given

From: A laminar augmented cascading flexible neural forest model for classification of cancer subtypes based on gene expression data

Parameter of LACFNForest Value Hyper-parameter of CNN Value
Population size 50 No. hidden layers Uncertain
Crossover probability 0.4 No. feature maps Uncertain
Mutation probability 0.01 No. nodes in hidden layers Uncertain
Level 4 Learning rate Uncertain
C1 2.0 Kernal size Uncertain
C2 2.0 Momentum Uncertain
Vmax 2.0 L1/L2 weight regularization penalty Uncertain
  1. “Uncertain” indicates that the default values of the parameters are unknown or usually require different settings for different tasks. These hyperparameters do not exist in our model, or exist but can be determined automatically during the optimization process of our model