Evolving Neo-fuzzy Neural Network with Adaptive Feature Selection

This paper suggests an approach to develop a class of evolving neural fuzzy networks with adaptive feature selection. The approach uses the neo-fuzzy neuron structure in conjunction with an incremental learning scheme that, simultaneously, selects the input variables, evolves the network structure, and updates the neural network weights. The mechanism of the adaptive feature selection uses statistical tests and information about the current model performance to decide if a new variable should be added, or if an existing variable should be excluded or kept as an input. The network structure evolves by adding or deleting membership functions and adapting its parameters depending of the input data and modeling error. The performance of the evolving neural fuzzy network with adaptive feature selection is evaluated considering instances of times series forecasting problems. Computational experiments and comparisons show that the proposed approach is competitive and achieves higher or as high performance as alternatives reported in the literature.