A Gegenbauer Neural Network with Regularized Weights Direct Determination for Classification

TL;DR

This paper introduces a Gegenbauer neural network (GNN) with regularized weight determination (R-WDD) that improves classification performance, robustness, and computational efficiency over traditional neural networks and ELMs by addressing their limitations.

Contribution

The paper proposes a novel Gegenbauer neural network with a regularized direct weight determination method, enhancing robustness and generalization in classification tasks.

Findings

GNN with R-WDD outperforms LS-SVM and ELM in accuracy and robustness.

The method demonstrates improved computational scalability and efficiency.

The approach effectively reduces overfitting and enhances generalization.

Abstract

Single-hidden layer feed forward neural networks (SLFNs) are widely used in pattern classification problems, but a huge bottleneck encountered is the slow speed and poor performance of the traditional iterative gradient-based learning algorithms. Although the famous extreme learning machine (ELM) has successfully addressed the problems of slow convergence, it still has computational robustness problems brought by input weights and biases randomly assigned. Thus, in order to overcome the aforementioned problems, in this paper, a novel type neural network based on Gegenbauer orthogonal polynomials, termed as GNN, is constructed and investigated. This model could overcome the computational robustness problems of ELM, while still has comparable structural simplicity and approximation capability. Based on this, we propose a regularized weights direct determination (R-WDD) based on equality-constrained optimization to determine the optimal output weights. The R-WDD tends to minimize the empirical risks and structural risks of the network, thus to lower the risk of over fitting and improve the generalization ability. This leads us to a the final GNN with R-WDD, which is a unified learning mechanism for binary and multi-class classification problems. Finally, as is verified in the various comparison experiments, GNN with R-WDD tends to have comparable (or even better) generalization performances, computational scalability and efficiency, and classification robustness, compared to least square support vector machine (LS-SVM), ELM with Gaussian kernel.