Graph Embedded Stochastic Configuration Networks for Imbalanced Data Classification

Abstract

This paper presents a novel approach called Graph Embedded Stochastic Configuration Networks (GESCNs) for complex data classification. To enhance the learning of geometric and discriminative features, especially in imbalanced and noisy datasets, the proposed GESCNs method integrates graph embedding strategies with Stochastic Configuration Networks (SCNs), a novel incremental randomized learning method known for its efficient model configuration mechanism. The intrinsic and penalty graph matrices help preserve topological relationships between data points, while SCNs ensure effective learning and strong generalization in modeling. Extensive experiments on diverse datasets demonstrate that GESCNs consistently outperform other randomized learning models across key metrics. These findings underscore the robustness of GESCNs in handling noisy and imbalanced data, making it a highly effective solution for challenging classification tasks.