@inproceedings{6a7bb8f389e3474a80f7385401157f33,
title = "Feature Selection for Fuzzy Neural Networks using Group Lasso Regularization",
abstract = "In this paper, a Group Lasso penalty based em-bedded/integrated feature selection method for multiple-input and multiple-output (MIMO) Takagi-Sugeno (TS) fuzzy neural network (FNN) is proposed. Group Lasso regularization can produce sparsity on the widths of the modified Gaussian membership function and this can guide us to select the useful features. Compared with Lasso, Group Lasso formulation has a Group penalty to the set of widths (weights) connected to a particular feature. To address the non-differentiability of the Group Lasso term, a smoothing Group Lasso method is introduced. Finally, one benchmark classification problem and two regression problems are used to validate the effectiveness of the proposed method.",
keywords = "Feature selection, Fuzzy neural network, Group Lasso",
author = "Tao Gao and Xiao Bai and Liang Zhang and Jian Wang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 ; Conference date: 05-12-2021 Through 07-12-2021",
year = "2021",
doi = "10.1109/SSCI50451.2021.9659548",
language = "英语",
series = "2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings",
address = "美国",
}