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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Published in | Optoelectronics letters Vol. 19; no. 5; pp. 284 - 289 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.05.2023
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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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. |
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ISSN: | 1673-1905 1993-5013 |
DOI: | 10.1007/s11801-023-2053-x |