Robust Decorrelated Stochastic Configuration Networks Ensemble via Weighted Negative Correlation Learning
针对随机配置网络处理含异常值数据时鲁棒性差的问题，提出一种鲁棒去相关集成模型，采用加权负相关学习和鲁棒正则化技术，在含高斯异常值的回归数据集上表现优于多种变体。
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, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm loss function combined with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> regularization 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.