Noise Elimination in Deep Random Vector Functional Link Network for Tabular Classification

Abstract

The Random Vector Functional Link Network (RVFL) is a single-layer feed-forward network characterized by randomised weights in its hidden layers. However, the randomness can introduce detrimental neurons, potentially impairing the network's performance. In response, this paper introduces multiple strategies to mitigate the noise from these randomised weights in RVFL networks. We first present a neuron normalization method that enhances latent space diversity and the network's resilience to input features. Additionally, we develop improved approaches incorporating various feature selection and elimination techniques. Furthermore, Bayesian Optimization is utilized to optimize hyperparameters within a defined space. The efficacy of these methods is demonstrated through results from UCI classification tasks, highlighting the statistically superior performance of our Noise Eliminated edRVFL (NE-edRVFL) with neuron normalization.