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Table 5 Algorithmic rules for flexible neural tree models

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


The output of the leaf node is the value of a given input feature variable


The output of a flexible neuron \(+M\)(non-leaf nodes) can be produced as formula (3) where \(I_j\) is the input to the current node, \(\omega _j\) is the corresponding weight and \(\theta\) is the node’s oset or bias


The output of each node is used as the input value of the node to which it is connected at the previous level


Calculate the value of the output vector from bottom to top, from leaf node to root node