Future of Information and Communication Conference (FICC) 2024
4-5 April 2024
Publication Links
IJACSA
Special Issues
Future of Information and Communication Conference (FICC)
Computing Conference
Intelligent Systems Conference (IntelliSys)
Future Technologies Conference (FTC)
International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 2, 2024.
Abstract: Robust broad learning system (RBLS) demonstrates the generalization and robustness for solving uncertain data regression tasks. To enhance representation ability of RBLS, this paper aims at developing a novel robust stacked broad learning system for solving noisy data regression problems, termed as RSBLS. In our work, we expand traditional BLS into a stacked broad learning system model with deep structure of feature nodes and enhancement nodes. Furthermore, ℓ1 norm loss function is employed to update the objective function of RSBLS for processing noisy data, we apply augmented Lagrange multiplier (ALM) to get the output weights of RSBLS which keeps the effectiveness and efficiency compared with weighted loss function. Simulation results over some regression datasets with outliers demonstrate that, the proposed RSBLS performs favorably with better robustness with respect to RVFL, BLS, Huber-WBLS, KDE-WBLS and RBLS.
Kai Zheng and Jie Liu, “A Novel Robust Stacked Broad Learning System for Noisy Data Regression” International Journal of Advanced Computer Science and Applications(IJACSA), 15(2), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150252
@article{Zheng2024,
title = {A Novel Robust Stacked Broad Learning System for Noisy Data Regression},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150252},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150252},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {2},
author = {Kai Zheng and Jie Liu}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.