arXiv:2607. 05635v1 Announce Type: new Abstract: Random Vector Functional Link (RVFL) networks are popular due to their fast training and universal approximation capabilities.
By Vrushank Ahire, Yogesh Kumar, M. A. Ganaie
arXiv:2607. 16728v1 Announce Type: new Abstract: The Broad Learning System (BLS) has been widely used for data classification and is based on a layer-by-layer feed-forward structure.
By Yogesh Kumar, Manju, Mudasir Ganaie
The paper introduces IFW-BLS, a Dual‑Robust Broad Learning System that enhances the traditional BLS by incorporating a bounded, asymmetric wave loss to protect against large residuals and by applying intuitionistic fuzzy scores to weight samples based on credibility. This dual approach mitigates the impact of noise, outliers, and ambiguous data points, while a Nesterov‑accelerated optimizer replaces the costly matrix inversion of conventional BLS. Experiments on UCI benchmarks and corruption tests demonstrate that IFW‑BLS outperforms baseline models and remains more stable under noisy conditions.
IFW-BLS is a Dual‑Robust Broad Learning System that enhances the standard Broad Learning System by incorporating two robustness mechanisms. It replaces the squared loss with a bounded, smooth, asymmetric wave loss to protect against large residuals, and applies intuitionistic fuzzy scores to weight samples based on global class consistency and local neighborhood conflict, thereby down‑weighting unreliable data. The model is optimized with a Nesterov accelerated gradient solver, avoiding explicit matrix inversion, and experiments on UCI benchmarks show it outperforms baseline models and remains stable under noise and outlier contamination.
By Mushir Akhtar, M. Tanveer
RoBell-RVFL is a lightweight, quality‑aware generalized bell random vector functional link network designed to address class imbalance and noisy data in real‑world datasets. It uses a dual‑strategy sample‑level weighting: unit weights preserve minority class information, while a probability‑weighted generalized bell membership function suppresses noisy majority samples in a kernel‑induced feature space. Experiments on UCI and KEEL benchmarks, including tests with up to 40% label noise, show that RoBell‑RVFL consistently outperforms recent RVFL variants, demonstrating the importance of adaptive, quality‑aware sample weighting for robust learning.
By A. Rahaman, A. Quadir, M. Tanveer
arXiv:2609. 20194v1 Announce Type: cross Abstract: Triangular membership functions (MFs) are widely used in fuzzy systems because of their interpretability, low parameterization complexity, and strong locality properties.
By Babak Sarani, Rahman Ardakanian, Ali Mousavi