@inproceedings{7ebd15836963448bb0ac139f5f98d05b,
title = "Redundancy-Controlled Feature Selection for Fuzzy Neural Networks",
abstract = "Group Lasso term has the group compression role on the value of the corresponding variables. In the fuzzy rule based system, for realizing fuzzification operation each feature corresponds to different number of membership functions. Inspired by the Group Lasso idea, the width of Gaussian membership function based Group Lasso regularization is designed to realize feature selection in the multiple inputs multiple outputs (MIMO) fuzzy neural network (FNN). To take into account the linear/nonlinear redundancy between features. Using the Pearson's correlation coefficient, a redundancy-controlled regularizer is also added into the traditional error function to further improve the performance of feature selection, i.e., control redundancy between features. We demonstrate the effectiveness of the strategy on three benchmark classification data sets.",
keywords = "Group Lasso, Pearson, feature redundancy, fuzzy neural network",
author = "Tao Gao and Xiao Bai and Liang Zhang and Chen Wang and Jian Wang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference on High Performance Big Data and Intelligent Systems, HPBD and IS 2021 ; Conference date: 05-12-2021 Through 07-12-2021",
year = "2021",
doi = "10.1109/HPBDIS53214.2021.9658345",
language = "英语",
series = "2021 International Conference on High Performance Big Data and Intelligent Systems, HPBD and IS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "172--176",
booktitle = "2021 International Conference on High Performance Big Data and Intelligent Systems, HPBD and IS 2021",
address = "美国",
}