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 (inpu...

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Bibliographic Details
Published inOptoelectronics letters Vol. 19; no. 5; pp. 284 - 289
Main Authors Yang, Jun, Xu, Yongyong, Wang, Bin, Li, Bo, Huang, Ming, Gao, Tao
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2023
Springer Nature B.V
Subjects
Artificial neural networks
Correlation coefficients
Error functions
Fuzzy logic
Lasers
Optical Devices
Optics
Optimization
Photonics
Physics
Physics and Astronomy
Redundancy
A
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Summary: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.
ISSN:1673-1905
1993-5013
DOI:10.1007/s11801-023-2053-x