Interpretable Constructive Algorithm for Enhancing Random Weight Neural Networks

The proposed interpretable constructive (IC) algorithm leverages geometric relationships to randomly assign hidden parameters, improving the interpretability of random weight neural networks (RWNNs). IC also employs a node pooling strategy to select high-quality hidden nodes that facilitate network convergence.