Robust Decorrelated Stochastic Configuration Networks Ensemble via Weighted Negative Correlation Learning
Chenglong Zhang, Chaoxun Guo, Xiaozhu Wang, Shifei Ding, Feng He Wu, David Zhang · IEEE Transactions on Systems Man and Cybernetics Systems · 2025
Stochastic configuration network (SCN) is a kind of incremental random neural network that assigns input weights and biases through data-dependent supervisory mechanism. However, the robustness of SCN is significantly reduced when processing the data disturbed by outliers. Aiming at improve the noisy data regression performance of SCN, this article presents a novel robust decorrelated SCNs ensemble model (RDSCNE). Such a robust decorrelated ensemble framework adopts weighted negative correlation learning (WNCL) and a robust regularization technique, which can guarantee the generalization performance for noisy data processing. Specifically, we first present a WNCL framework based on kernel density estimation (KDE) to build SCNs ensemble model, so that the negative effects of noise can be suppressed through KDE to calculate penalty weights of each training sample for the computation of ensemble weights. Meanwhile,l1norm loss function combined withl2regularization technique is employed as the objective function of base components. This approach is designed to process outliers with sparse characteristics and alleviate the over-fitting phenomenon. Then, augmented Lagrange multiplier (ALM) method is used to calculate the objective function. Experimental results over some regression datasets with Gaussian outliers demonstrate that the proposed RDSCNE model has better robustness than the various SCN variants.