Group Lasso based redundancy-controlled feature selection for fuzzy neural network

If there are a lot of inputs,the readability of the “If-then” fuzzy rule is reduced,and the complexity of the fuzzy neural network structure will be increased.Hence,to optimize the structure of the fuzzy rule based neural network,a group Lasso based redundancy-controlled feature selection(input pruning) method is proposed.For realizing feature selection,the linear/nonlinear redundancy between features is considered,and the Pearson’s correlation coefficient is employed to construct the additive redundancy-controlled regularizer in the error function.In addition,considering the past gradient information,a novel parameter optimization method is presented.Finally,we demonstrate the effectiveness of our method on two benchmark classification datasets.