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

Jun Yang , Yongyong Xu , Bin Wang , Bo Li , Ming Huang , Tao Gao

Optoelectronics Letters ›› 2023, Vol. 19 ›› Issue (5) : 284 -289.

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Optoelectronics Letters ›› 2023, Vol. 19 ›› Issue (5) : 284 -289. DOI: 10.1007/s11801-023-2053-x
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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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Jun Yang, Yongyong Xu, Bin Wang, Bo Li, Ming Huang, Tao Gao. Group Lasso based redundancy-controlled feature selection for fuzzy neural network. Optoelectronics Letters, 2023, 19(5): 284-289 DOI:10.1007/s11801-023-2053-x

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