R^2VFL: A Robust Random Vector Functional Link Network with Huber-Weighted Framework

TL;DR

The paper tackles RVFL sensitivity to noise and outliers by introducing R$^2$VFL, a robust RVFL framework that combines a Huber weighting scheme with a class-probability mechanism. It defines a sample-contribution score and two class-center strategies (mean and median) to yield R$^2$VFL-A and R$^2$VFL-M, with an analytically solvable training objective. Extensive experiments on 47 UCI datasets and EEG data show that the proposed variants outperform strong RVFL baselines in binary, multiclass, and practical biomedical tasks, with statistical tests confirming significance. The work offers a robust, efficient alternative to gradient-based training for randomized neural networks and points to future deep and ensemble extensions as well as matrix-input handling.

Abstract

The random vector functional link (RVFL) neural network has shown significant potential in overcoming the constraints of traditional artificial neural networks, such as excessive computation time and suboptimal solutions. However, RVFL faces challenges when dealing with noise and outliers, as it assumes all data samples contribute equally. To address this issue, we propose a novel robust framework, R2VFL, RVFL with Huber weighting function and class probability, which enhances the model's robustness and adaptability by effectively mitigating the impact of noise and outliers in the training data. The Huber weighting function reduces the influence of outliers, while the class probability mechanism assigns less weight to noisy data points, resulting in a more resilient model. We explore two distinct approaches for calculating class centers within the R2VFL framework: the simple average of all data points in each class and the median of each feature, the later providing a robust alternative by minimizing the effect of extreme values. These approaches give rise to two novel variants of the model-R2VFL-A and R2VFL-M. We extensively evaluate the proposed models on 47 UCI datasets, encompassing both binary and multiclass datasets, and conduct rigorous statistical testing, which confirms the superiority of the proposed models. Notably, the models also demonstrate exceptional performance in classifying EEG signals, highlighting their practical applicability in real-world biomedical domain.