Archive > Volume 19 Issue 5 > 2023,19(5):284-289. DOI:https://doi.org/10.1007/s11801-023-2053-x CSTR:[cstr] Prev Next
Group Lasso based redundancy-controlled feature selection for fuzzy neural network
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Abstract:
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.
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