KANFIS: A Neuro-Symbolic Framework for Interpretable and Uncertainty-Aware Learning
KANFIS is a new neuro-symbolic architecture that combines neural network learning with fuzzy logic reasoning to create interpretable models. It uses an additive function decomposition approach to manage complexity, allowing it to handle high-dimensional data better than traditional methods while explicitly modeling uncertainty.